Detecting Identified Information in Privacy Firewalls

ABSTRACT

Systems, methods and non-transitory computer readable media for detecting identified information in privacy firewalls are provided. A repeating field in a data collection may be analyzed to determine whether the field is likely to include information that identifies particular individuals. An access request of a user may be received. A permission record associated with the user may be accessed. In response to the field being likely to include information that identifies particular individuals and a first value in the permission record, access to the field may be denied, in response to the field not being likely to include information that identifies particular individuals and the first value in the permission record, access to the field may be provided, and in response to a second value in the permission record, access to the field may be provided.

CROSS REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application No. 63/026,154, filed on May 18, 2020, and U.S.Provisional Patent Application No. 63/125,978, filed on Dec. 16, 2020.

The entire contents of all of the above-identified applications areherein incorporated by reference.

BACKGROUND Technological Field

The disclosed embodiments generally relate to systems and methods fordetecting identified information. More particularly, the disclosedembodiments relate to systems and methods for detecting identifiedinformation in privacy firewalls.

Background Information

Numerous medical records are created, read and edited by vast number ofmedical care providers. Nowadays, some medical research requires accessto large datasets of medical information. However, accessing medicaldata may prove challenging, partly due to regulatory and privacyrequirements. Easing access to medical data may facilitate acceleratedmedical research.

SUMMARY

Embodiments consistent with the present disclosure provide systems,methods, and devices for providing information based on private medicaldata.

In some embodiments, systems, methods and non-transitory computerreadable media for enabling graphical illustration of private medicalinformation are provided. In some examples, a subgroup defining inputmay be received, the subgroup defining input may be based on a firstinput from a user and may define a subgroup of a group of patients.Further, in some examples, a statistical query about the subgroup of thegroup of patients may be received, the statistical query may be based ona second input from the user. Further, in some examples, a size of thesubgroup of the group of patients may be determined. Further, in someexamples, the determined size of the subgroup of the group of patientsmay be compared with a selected subgroup size threshold. Further, insome examples, first information may be provided. For example, the firstinformation may be based on the statistical query and may be configuredto enable a presentation of a graphical illustration of an estimatedproperty of the subgroup of the group of patients in response to thesecond input from the user. In some examples, in response to thedetermined size of the subgroup of the group of patients being largerthan the selected subgroup size threshold, the first information may beprovided, and in response to the determined size of the subgroup of thegroup of patients being smaller than the selected subgroup sizethreshold, providing the first information may be withheld and/orforwent.

In some embodiments, systems, methods and non-transitory computerreadable media for facilitating privacy preserving joint medicalresearch are provided. For example, systems and method for selectivelyproviding information about medical data are provided.

In some examples, a first statistical query about medical data may bereceived, the first statistical query may be based on an input from afirst user. Further, in some examples, a first estimated property of themedical data may be provided to the first user, the first estimatedproperty of the medical data may be based on the first statisticalquery. Further, in some examples, a second statistical query about themedical data may be received, the second statistical query may be basedon an input from a second user (the second user may differ from thefirst user). Further, in some examples, a first group of users thatincludes the first user may be selected. Further, in some examples, itmay be determined whether the first group of users includes the seconduser. Further, in some examples, in response to a determination that thefirst group of users does not include the second user, a secondestimated property of the medical data may be provided to the seconduser (the second estimated property of the medical data may be based onthe second statistical query), and in response to a determination thatthe first group of users includes the second user, providing the secondestimated property of the medical data to the second user may bewithheld and/or forwent. In some examples, the first estimated propertyof the medical data may be a first actual property of the medical data.In some examples, the first group of users may be selected of aplurality of alternative groups of users based on an identity of thefirst user. In some examples, a type of the first user may be aparticular type, and the selected first group of users may include allusers of the particular type from a particular plurality of users.

In some embodiments, systems, methods and non-transitory computerreadable media for controlling access to private medical information areprovided.

In some examples, a request to access a content of an element may bereceived, the content of the element may include at least a firstportion and a second portion, the first portion may include identifiableinformation and the second portion may include no identifiableinformation. A permission record corresponding to the element may beaccessed. In response to a first value in the permission record, accessto the content of the element may be provided, including access to thefirst portion and the second portion of the content of the element, andin response to a second value in the permission record, partial accessto the content of the element may be provided, the partial access mayinclude access to the second portion of the content of the element andmay exclude access to the first portion of the content of the element.

In some embodiments, systems, methods and non-transitory computerreadable media for ownership determination are provided.

In some examples, a request of a user to perform an action for creatinga new data collection using one or more source data collections may bereceived. One or more ownership records may be accessed to determineownership status of the one or more source data collections. One or morepermission records may be accessed to determine permission status of theuser in relation to the one or more source data collections. In responseto a determination that the user does not have permission to view atleast part of at least one of the one or more source data collectionsand that the user is not an owner of the at least one of the one or moresource data collections, it may be determined that the user is not anowner of the new data collection, and in response to a determinationthat for each data collection of the one or more source data collectionsthe user is at least one of an owner of the data collection or haspermission to view the entire data collection, it may be determined thatthe user is an owner of the new data collection.

In some embodiments, systems, methods and non-transitory computerreadable media for determining permissions are provided.

In some examples, at least part of a content of a data collection may beanalyzed to determine a subject matter. A permission corresponding tothe data collection and at least one user may be determined based on thesubject matter. A request of the at least one user may be received toaccess at least part of the data collection. In response to a firstdetermined permission, the requested access to the at least part of thedata collection may be provided, and in response to a second determinedpermission, the request may be denied.

In some embodiments, systems, methods and non-transitory computerreadable media for detecting identified information are provided.

In some examples, a data collection may be accessed to identify arepeating field in the data collection. Content of the field in the datacollection may be analyzed to determine whether the field is likely toinclude information that identifies at least one particular individual.An access request of a user may be received. A permission recordassociated with the user may be accessed. In response to a determinationthat the field is likely to include information that identifies at leastone particular individual and a first value in the permission record,access of the user to at least part of the content of the field in thedata collection may be denied, in response to a determination that thefield is not likely to include information that identifies at least oneparticular individual and the first value in the permission record,access of the user to the at least part of the content of the field inthe data collection may be provided, and in response to a second valuein the permission record, access of the user to the at least part of thecontent of the field in the data collection may be provided.

Consistent with other disclosed embodiments, a non-transitory computerreadable medium may store software programs, each software programcomprising data and computer implementable instructions for carrying outany of the methods described herein. For example, when the softwareprogram is executed by at least one processing device, it may beconfigured to perform any of the methods described herein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary system for providinginformation based on medical data.

FIGS. 2A and 2B are block diagrams illustrating some possibleimplementations of a computing device.

FIG. 3 is a block diagram illustrating a possible implementation of acommunicating system.

FIGS. 4A and 4B are block diagrams illustrating some possibleimplementations of a cloud platform.

FIG. 5 is a block diagram illustrating a possible implementation of acomputational node.

FIG. 6 is a block diagrams illustrating a possible ecosystem.

FIG. 7A illustrates an exemplary embodiment of a memory storing aplurality of modules.

FIG. 7B illustrates an exemplary embodiment of a data element.

FIG. 7C illustrates an exemplary embodiment of a data element.

FIG. 8A illustrates an example of a method for enabling graphicalillustration based on private medical information.

FIG. 8B illustrates an example of a method for determining an estimatedproperty of a subgroup of a group of patients.

FIG. 8C illustrates an example of a method for enabling graphicalillustration based on private medical information.

FIG. 8D illustrates an example of a method for enabling graphicalillustration based on private medical information.

FIG. 8E illustrates an example of a method for enabling graphicalillustration based on private medical information.

FIG. 8F illustrates an example of a method for enabling graphicalillustration based on private medical information.

FIG. 9A illustrates an example of a method for selectively providinginformation about medical data.

FIG. 9B illustrates an example of a method for determining an estimatedproperty of medical data.

FIG. 9C illustrates an example of a method for selectively providinginformation about medical data.

FIG. 9D illustrates an example of a method for selectively providinginformation about medical data.

FIG. 9E illustrates an example of a method for selectively providinginformation about medical data.

FIG. 9F illustrates an example of a method for selectively providinginformation about medical data.

FIG. 10 illustrates an example of a method for controlling access toprivate medical information.

FIG. 11 illustrates an example of a method for ownership determination.

FIG. 12 illustrates an example of a method for determining permissions.

FIG. 13 illustrates an example of a method for detecting identifiedinformation.

DESCRIPTION

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “calculating”,“computing”, “determining”, “generating”, “setting”, “configuring”,“selecting”, “defining”, “applying”, “obtaining”, “monitoring”,“providing”, “identifying”, “segmenting”, “classifying”, “analyzing”,“associating”, “extracting”, “storing”, “receiving”, “transmitting”, orthe like, include action and/or processes of a computer that manipulateand/or transform data into other data, said data represented as physicalquantities, for example such as electronic quantities, and/or said datarepresenting the physical objects. The terms “computer”, “processor”,“controller”, “processing unit”, “computing device”, and “processingmodule” should be expansively construed to cover any kind of electronicdevice, component or unit with data processing capabilities, including,by way of non-limiting example, a personal computer, a wearablecomputer, a tablet, a smartphone, a server, a computing system, a cloudcomputing platform, a communication device, a processor (for example,digital signal processor (DSP), an image signal processor (ISR), amicrocontroller, a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), a central processing unit (CPA), agraphics processing unit (GPU), a visual processing unit (VPU), and soon), possibly with embedded memory, a single core processor, a multicore processor, a core within a processor, any other electroniccomputing device, or any combination of the above.

The operations in accordance with the teachings herein may be performedby a computer specially constructed or programmed to perform thedescribed functions.

As used herein, the phrase “for example,” “such as”, “for instance” andvariants thereof describe non-limiting embodiments of the presentlydisclosed subject matter. Reference in the specification to “one case”,“some cases”, “other cases” or variants thereof means that a particularfeature, structure or characteristic described in connection with theembodiment(s) may be included in at least one embodiment of thepresently disclosed subject matter. Thus the appearance of the phrase“one case”, “some cases”, “other cases” or variants thereof does notnecessarily refer to the same embodiment(s). As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

It is appreciated that certain features of the presently disclosedsubject matter, which are, for clarity, described in the context ofseparate embodiments, may also be provided in combination in a singleembodiment. Conversely, various features of the presently disclosedsubject matter, which are, for brevity, described in the context of asingle embodiment, may also be provided separately or in any suitablesub-combination.

In embodiments of the presently disclosed subject matter, one or morestages illustrated in the figures may be executed in a different orderand/or one or more groups of stages may be executed simultaneously andvice versa. The figures illustrate a general schematic of the systemarchitecture in accordance embodiments of the presently disclosedsubject matter. Each module in the figures can be made up of anycombination of software, hardware and/or firmware that performs thefunctions as defined and explained herein. The modules in the figuresmay be centralized in one location or dispersed over more than onelocation.

It should be noted that some examples of the presently disclosed subjectmatter are not limited in application to the details of construction andthe arrangement of the components set forth in the following descriptionor illustrated in the drawings. The invention can be capable of otherembodiments or of being practiced or carried out in various ways. Also,it is to be understood that the phraseology and terminology employedherein is for the purpose of description and should not be regarded aslimiting.

In this document, an element of a drawing that is not described withinthe scope of the drawing and is labeled with a numeral that has beendescribed in a previous drawing may have the same use and description asin the previous drawings.

The drawings in this document may not be to any scale. Different figuresmay use different scales and different scales can be used even withinthe same drawing, for example different scales for different views ofthe same object or different scales for the two adjacent objects.

FIG. 1 is an illustration of an exemplary system 100 for providinginformation based on medical data. In some examples, system 100 mayinclude one or more medical organizations 110 (in this example, medicalorganizations 110A, 110B and 110C). Some possible examples of suchmedical organizations 110 may include hospitals, medical clinics,medical labs, pharmacies, medical care providers 630 (described below),insurers 640 (described below), regulators 650 (described below), and soforth. Each one of the one or more medical organizations 110 may holdprivate medical data. In this example, medical organization 110A holdsprivate medical data 112. Access to all or portions of private medicaldata 112 may be restricted, for example due to regulatory and privacyrequirements, due to medical organization 110 procedures, and so forth.Some examples of such medical data 112 may include medical records 702(described below), scheduling records 704 (described below), financialrecords 706 (described below), insurance records 708 (described below),research records 710 (described below), and so forth. In some examples,system 100 may include public data 120. Access to public data may bepublically available to everyone. In some examples, system 100 mayinclude one or more teams 140 (in this example, teams 140A, 140B and140C). Some possible examples of such teams 140 may include researchteams, research organizations, individual researchers, researchers 660(described below), insurers 640 (described below), regulators 650(described below), and so forth. In some example, each one of the one ormore teams 140 may hold proprietary medical data. In this example, team140A holds proprietary medical data 142. In some example, each one ofthe one or more teams 140 may use computerized data analysis devices. Inthis example, team 140A uses computerized data analysis device 144. Somepossible implementations of computerized data analysis device 144 mayinclude computing device 200 (described below), cloud platform 400(described below), computational node 500 (described below), and soforth. In some example, each one of the one or more teams 140 mayinclude one or more users. In this example, team 140A includes users146, 147 and 148. Some possible examples of such users may includeresearchers 660 (described below), human data analysts, automated dataanalyst processes, and so forth. In some examples, data may be exchangedamong elements of system 100, for example through communication network130. Examples of communication network 130 may include the Internet,phone networks, cellular networks, satellite communication networks,private communication networks, virtual private networks (VPN), and soforth. Computerized devices (such as computing device 200, cloudplatform 400, computational node 500, computerized data analysis device144, storage devices, local storage, remote storage, network attachedstorage, etc.) may connect to communication network 130 directly,through local router, through wireless communication, through wiredcommunication, and so forth.

In some examples, at least a portion of at least one of private medicaldata 112, public data 120 and proprietary medical data 142 may be storedin memory (such as memory 700, memory units 210, memory modules 410,etc.), in storage device (such as local storage, remote storage, networkattached storage, etc.), and so forth. In some examples, at least aportion of at least one of private medical data 112, public data 120 andproprietary medical data 142 may be managed and/or controlled and/ormaintained and/or collected and/or analyzed using local computingdevices (such as computing device 200), local computerized servers,remote computerized servers, private and/or public cloud platforms (suchas cloud platform 400), computational node (such as computational node500), and so forth.

In some embodiments, a privacy firewall may be used to control access todata and enforce privacy rules. For example, a privacy firewall may beimplemented using a computing device (such as computing device 200), maybe implemented using a cloud platform (such as cloud platform 400), maybe implemented as a software (for example in an operation system, as asoftware configured to be installed on a computing device, etc.), and soforth. In one example, a privacy firewall may be positioned on theconnection between medical organization 110A and external network 130,for example to control all access to data in medical organization 110Afrom external entities and enforce privacy rules on access to data inmedical organization 110A through network 130. In another example, aprivacy firewall may be positioned on the connection between team 140Aand external network 130, for example to control all access to externaldata from team 140A and enforce privacy rules on access of team 140A todata through network 130. In yet another example, a privacy firewall maybe positioned within medical organization 110A to control access toprivate medical data 112, for example to control all access to privatemedical data 112, whether the access is coming from within medicalorganization 110A or from external sources and enforce privacy rules onaccess to private medical data 112. In an additional example, a privacyfirewall may be installed on computerized data analysis device 144, forexample to control all access to data from computerized data analysisdevice 144 and enforce privacy rules on access of computerized dataanalysis device 144 to data.

FIG. 2A is a block diagram illustrating a possible implementation ofcomputing device 200. In this example, computing device 200 maycomprise: one or more memory units 210, one or more processing units220, and one or more communication modules 230. In some implementations,computing device 200 may comprise additional components, while somecomponents listed above may be excluded.

FIG. 2B is a block diagram illustrating a possible implementation ofcomputing device 200. In this example, computing device 200 maycomprise: one or more memory units 210, one or more processing units220, one or more communication modules 230, one or more power sources240, one or more audio sensors 250, one or more image sensors 260, oneor more light sources 265, one or more motion sensors 270, and one ormore positioning sensors 275. In some implementations, computing device200 may comprise additional components, while some components listedabove may be excluded. For example, in some implementations computingdevice 200 may also comprise at least one of the following: one or morebarometers; one or more user input devices; one or more output devices;and so forth. In another example, in some implementations at least oneof the following may be excluded from computing device 200: memory units210, communication modules 230, power sources 240, audio sensors 250,image sensors 260, light sources 265, motion sensors 270, andpositioning sensors 275.

In some embodiments, one or more power sources 240 may be configured to:power computing device 200, power cloud platform 400, and/or powercomputational node 500. Possible implementation examples of powersources 240 may include: one or more electric batteries; one or morecapacitors; one or more connections to external power sources; one ormore power convertors; any combination of the above; and so forth.

In some embodiments, the one or more processing units 220 may beconfigured to execute software programs. For example, processing units220 may be configured to execute software programs stored on the memoryunits 210. In some cases, the executed software programs may storeinformation in memory units 210. In some cases, the executed softwareprograms may retrieve information from the memory units 210. Possibleimplementation examples of the processing units 220 may include: one ormore single core processors, one or more multicore processors; one ormore controllers; one or more application processors; one or more systemon a chip processors; one or more central processing units; one or moregraphical processing units; one or more neural processing units; anycombination of the above; and so forth.

In some embodiments, the one or more communication modules 230 may beconfigured to receive and transmit information. For example, controlsignals may be transmitted and/or received through communication modules230. In another example, information received though communicationmodules 230 may be stored in memory units 210. In an additional example,information retrieved from memory units 210 may be transmitted usingcommunication modules 230. In another example, input data may betransmitted and/or received using communication modules 230. Examples ofsuch input data may include: input data inputted by a user using userinput devices; information captured using one or more sensors; and soforth. Examples of such sensors may include: audio sensors 250; imagesensors 260; motion sensors 270; positioning sensors 275; chemicalsensors; temperature sensors; barometers; and so forth.

In some embodiments, the one or more audio sensors 250 may be configuredto capture audio by converting sounds to digital information. Someexamples of audio sensors 250 may include: microphones, unidirectionalmicrophones, bidirectional microphones, cardioid microphones,omnidirectional microphones, onboard microphones, wired microphones,wireless microphones, any combination of the above, and so forth. Insome examples, the captured audio may be stored in memory units 210. Insome additional examples, the captured audio may be transmitted usingcommunication modules 230, for example to other computerized devices,such as cloud platform 400, computational node 500, and so forth. Insome examples, processing units 220 may control the above processes. Forexample, processing units 220 may control at least one of: capturing ofthe audio; storing the captured audio; transmitting of the capturedaudio; and so forth. In some cases, the captured audio may be processedby processing units 220. For example, the captured audio may becompressed by processing units 220; possibly followed: by storing thecompressed captured audio in memory units 210; by transmitted thecompressed captured audio using communication modules 230; and so forth.In another example, the captured audio may be processed using speechrecognition algorithms. In another example, the captured audio may beprocessed using speaker recognition algorithms.

In some embodiments, an image sensor 260 may include a device configuredto capture images, a sequence of images, videos, and so forth. Thisincludes sensors that convert optical input into images, where opticalinput can be visible light (like in a camera), radio waves, microwaves,terahertz waves, ultraviolet light, infrared light, x-rays, gamma rays,and/or any other light spectrum. This also includes both 2D and 3Dsensors. Examples of image sensor technologies may include: CCD, CMOS,NMOS, and so forth. 3D sensors may be implemented using differenttechnologies, including: stereo camera, active stereo camera, time offlight camera, structured light camera, radar, range image camera, andso forth. In some examples, the one or more image sensors 260 may beconfigured to capture visual information by converting light to: images;sequence of images; videos; 3D images; sequence of 3D images; 3D videos;and so forth. In some examples, the captured visual information may bestored in memory units 210. In some additional examples, the capturedvisual information may be transmitted using communication modules 230,for example to other computerized devices, such as cloud platform 400,computational node 500, and so forth. In some examples, processing units220 may control the above processes. For example, processing units 220may control at least one of: capturing of the visual information;storing the captured visual information; transmitting of the capturedvisual information; and so forth. In some cases, the captured visualinformation may be processed by processing units 220. For example, thecaptured visual information may be compressed by processing units 220;possibly followed: by storing the compressed captured visual informationin memory units 210; by transmitted the compressed captured visualinformation using communication modules 230; and so forth. In anotherexample, the captured visual information may be processed in order to:detect objects, detect events, detect action, detect face, detectpeople, recognize person, and so forth.

In some embodiments, the one or more light sources 265 may be configuredto emit light, for example in order to enable better image capturing byimage sensors 260. In some examples, the emission of light may becoordinated with the capturing operation of image sensors 260. In someexamples, the emission of light may be continuous. In some examples, theemission of light may be performed at selected times. The emitted lightmay be visible light, infrared light, x-rays, gamma rays, and/or in anyother light spectrum. In some examples, image sensors 260 may capturelight emitted by light sources 265, for example in order to capture 3Dimages and/or 3D videos using active stereo method.

In some embodiments, the one or more motion sensors 270 may beconfigured to perform at least one of the following: detect motion ofobjects in the environment of computing device 200; measure the velocityof objects in the environment of computing device 200; measure theacceleration of objects in the environment of computing device 200;detect motion of computing device 200; measure the velocity of computingdevice 200; measure the acceleration of computing device 200; and soforth. In some implementations, the one or more motion sensors 270 maycomprise one or more accelerometers configured to detect changes inproper acceleration and/or to measure proper acceleration of computingdevice 200. In some implementations, the one or more motion sensors 270may comprise one or more gyroscopes configured to detect changes in theorientation of computing device 200 and/or to measure informationrelated to the orientation of computing device 200. In someimplementations, motion sensors 270 may be implemented using imagesensors 260, for example by analyzing images captured by image sensors260 to perform at least one of the following tasks: track objects in theenvironment of computing device 200; detect moving objects in theenvironment of computing device 200; measure the velocity of objects inthe environment of computing device 200; measure the acceleration ofobjects in the environment of computing device 200; measure the velocityof computing device 200, for example by calculating the egomotion ofimage sensors 260; measure the acceleration of computing device 200, forexample by calculating the egomotion of image sensors 260; and so forth.In some implementations, motion sensors 270 may be implemented usingimage sensors 260 and light sources 265, for example by implementing aLIDAR using image sensors 260 and light sources 265. In someimplementations, motion sensors 270 may be implemented using one or moreRADARs. In some examples, information captured using motion sensors 270:may be stored in memory units 210, may be processed by processing units220, may be transmitted and/or received using communication modules 230,and so forth.

In some embodiments, the one or more positioning sensors 275 may beconfigured to obtain positioning information of computing device 200, todetect changes in the position of computing device 200, and/or tomeasure the position of computing device 200. In some examples,positioning sensors 275 may be implemented using one of the followingtechnologies: Global Positioning System (GPS), GLObal NAvigationSatellite System (GLONASS), Galileo global navigation system, BeiDounavigation system, other Global Navigation Satellite Systems (GNSS),Indian Regional Navigation Satellite System (IRNSS), Local PositioningSystems (LPS), Real-Time Location Systems (RTLS), Indoor PositioningSystem (IPS), Wi-Fi based positioning systems, cellular triangulation,and so forth. In some examples, information captured using positioningsensors 275 may be stored in memory units 210, may be processed byprocessing units 220, may be transmitted and/or received usingcommunication modules 230, and so forth.

In some embodiments, the one or more chemical sensors may be configuredto perform at least one of the following: measure chemical properties inthe environment of computing device 200; measure changes in the chemicalproperties in the environment of computing device 200; detect thepresent of chemicals in the environment of computing device 200; measurethe concentration of chemicals in the environment of computing device200. Examples of such chemical properties may include: pH level,toxicity, temperature, and so forth. Examples of such chemicals mayinclude: electrolytes, particular enzymes, particular hormones,particular proteins, smoke, carbon dioxide, carbon monoxide, oxygen,ozone, hydrogen, hydrogen sulfide, and so forth. In some examples,information captured using chemical sensors may be stored in memoryunits 210, may be processed by processing units 220, may be transmittedand/or received using communication modules 230, and so forth.

In some embodiments, the one or more temperature sensors may beconfigured to detect changes in the temperature of the environment ofcomputing device 200 and/or to measure the temperature of theenvironment of computing device 200. In some examples, informationcaptured using temperature sensors may be stored in memory units 210,may be processed by processing units 220, may be transmitted and/orreceived using communication modules 230, and so forth.

In some embodiments, the one or more barometers may be configured todetect changes in the atmospheric pressure in the environment ofcomputing device 200 and/or to measure the atmospheric pressure in theenvironment of computing device 200. In some examples, informationcaptured using the barometers may be stored in memory units 210, may beprocessed by processing units 220, may be transmitted and/or receivedusing communication modules 230, and so forth.

In some embodiments, the one or more user input devices may beconfigured to allow one or more users to input information. In someexamples, user input devices may comprise at least one of the following:a keyboard, a mouse, a touch pad, a touch screen, a joystick, amicrophone, an image sensor, and so forth. In some examples, the userinput may be in the form of at least one of: text, sounds, speech, handgestures, body gestures, tactile information, and so forth. In someexamples, the user input may be stored in memory units 210, may beprocessed by processing units 220, may be transmitted and/or receivedusing communication modules 230, and so forth.

In some embodiments, the one or more user output devices may beconfigured to provide output information to one or more users. In someexamples, such output information may comprise of at least one of:notifications, feedbacks, reports, and so forth. In some examples, useroutput devices may comprise at least one of: one or more audio outputdevices; one or more textual output devices; one or more visual outputdevices; one or more tactile output devices; and so forth. In someexamples, the one or more audio output devices may be configured tooutput audio to a user, for example through: a headset, a set ofspeakers, and so forth. In some examples, the one or more visual outputdevices may be configured to output visual information to a user, forexample through: a display screen, an augmented reality display system,a printer, a LED indicator, and so forth. In some examples, the one ormore tactile output devices may be configured to output tactilefeedbacks to a user, for example through vibrations, through motions, byapplying forces, and so forth. In some examples, the output may beprovided: in real time, offline, automatically, upon request, and soforth. In some examples, the output information may be read from memoryunits 210, may be provided by a software executed by processing units220, may be transmitted and/or received using communication modules 230,and so forth.

FIG. 3 is a block diagram illustrating a possible implementation of acommunicating system. In this example, computing devices 200 a, 200 band 200 c may communicate with cloud platform 400 and/or with each otherthrough communication network 130. Possible implementations of computingdevices 200 a, 200 b and 200 c may include computing device 200 asdescribed in FIGS. 2A and/or 2B. Some possible implementations of cloudplatform 400 are described in FIGS. 4A, 4B and 5.

FIG. 3 illustrates a possible implementation of a communication system.In some embodiments, other communication systems that enablecommunication among computing devices and/or between a computing device(such as computing device 200) and a cloud platform (such as cloudplatform 400) may be used.

FIG. 4A is a block diagram illustrating a possible implementation ofcloud platform 400. In this example, cloud platform 400 may comprisecomputational node 500 a, computational node 500 b, computational node500 c and computational node 500 d. In some examples, a possibleimplementation of computational nodes 500 a, 500 b, 500 c and 500 d maycomprise computing device 200. In some examples, a possibleimplementation of computational nodes 500 a, 500 b, 500 c and 500 d maycomprise computational node 500 as described in FIG. 5.

FIG. 4B is a block diagram illustrating a possible implementation ofcloud platform 400. In this example, cloud platform 400 may comprise:one or more computational nodes 500, one or more shared memory modules410, one or more power sources 240, one or more node registrationmodules 420, one or more load balancing modules 430, one or moreinternal communication modules 440, and one or more externalcommunication modules 450. In some implementations, cloud platform 400may comprise additional components, while some components listed abovemay be excluded. For example, in some implementations cloud platform 400may also comprise at least one of the following: one or more user inputdevices; one or more output devices; and so forth. In another example,in some implementations at least one of the following may be excludedfrom cloud platform 400: shared memory modules 410, power sources 240,node registration modules 420, load balancing modules 430, internalcommunication modules 440, and external communication modules 450.

FIG. 5 is a block diagram illustrating a possible implementation ofcomputational node 500. In this example, computational node 500 maycomprise: one or more memory units 210, one or more processing units220, one or more shared memory access modules 510, one or more powersources 240, one or more internal communication modules 440, and one ormore external communication modules 450. In some implementations,computational node 500 may comprise additional components, while somecomponents listed above may be excluded. For example, in someimplementations computational node 500 may also comprise at least one ofthe following: one or more user input devices; one or more outputdevices; and so forth. In another example, in some implementations atleast one of the following may be excluded from computational node 500:memory units 210, shared memory access modules 510, power sources 240,internal communication modules 440, and external communication modules450.

In some embodiments, internal communication modules 440 and externalcommunication modules 450 may be implemented as a combined communicationmodule, such as communication modules 230. In some embodiments, onepossible implementation of cloud platform 400 may comprise computingdevice 200. In some embodiments, one possible implementation ofcomputational node 500 may comprise computing device 200. In someembodiments, one possible implementation of shared memory access modules510 may comprise using internal communication modules 440 to sendinformation to shared memory modules 410 and/or receive information fromshared memory modules 410. In some embodiments, node registrationmodules 420 and load balancing modules 430 may be implemented as acombined module.

In some embodiments, the one or more shared memory modules 410 may beaccessed by more than one computational node. Therefore, shared memorymodules 410 may allow information sharing among two or morecomputational nodes 500. In some embodiments, the one or more sharedmemory access modules 510 may be configured to enable access ofcomputational nodes 500 and/or the one or more processing units 220 ofcomputational nodes 500 to shared memory modules 410. In some examples,computational nodes 500 and/or the one or more processing units 220 ofcomputational nodes 500, may access shared memory modules 410, forexample using shared memory access modules 510, in order to perform atleast one of: executing software programs stored on shared memorymodules 410, store information in shared memory modules 410, retrieveinformation from the shared memory modules 410.

In some embodiments, the one or more node registration modules 420 maybe configured to track the availability of the computational nodes 500.In some examples, node registration modules 420 may be implemented as: asoftware program, such as a software program executed by one or more ofthe computational nodes 500; a hardware solution; a combined softwareand hardware solution; and so forth. In some implementations, noderegistration modules 420 may communicate with computational nodes 500,for example using internal communication modules 440. In some examples,computational nodes 500 may notify node registration modules 420 oftheir status, for example by sending messages: at computational node 500startup; at computational node 500 shutdown; at constant intervals; atselected times; in response to queries received from node registrationmodules 420; and so forth. In some examples, node registration modules420 may query about computational nodes 500 status, for example bysending messages: at node registration module 420 startup; at constantintervals; at selected times; and so forth.

In some embodiments, the one or more load balancing modules 430 may beconfigured to divide the work load among computational nodes 500. Insome examples, load balancing modules 430 may be implemented as: asoftware program, such as a software program executed by one or more ofthe computational nodes 500; a hardware solution; a combined softwareand hardware solution; and so forth. In some implementations, loadbalancing modules 430 may interact with node registration modules 420 inorder to obtain information regarding the availability of thecomputational nodes 500. In some implementations, load balancing modules430 may communicate with computational nodes 500, for example usinginternal communication modules 440. In some examples, computationalnodes 500 may notify load balancing modules 430 of their status, forexample by sending messages: at computational node 500 startup; atcomputational node 500 shutdown; at constant intervals; at selectedtimes; in response to queries received from load balancing modules 430;and so forth. In some examples, load balancing modules 430 may queryabout computational nodes 500 status, for example by sending messages:at load balancing module 430 startup; at constant intervals; at selectedtimes; and so forth.

In some embodiments, the one or more internal communication modules 440may be configured to receive information from one or more components ofcloud platform 400, and/or to transmit information to one or morecomponents of cloud platform 400. For example, control signals and/orsynchronization signals may be sent and/or received through internalcommunication modules 440. In another example, input information forcomputer programs, output information of computer programs, and/orintermediate information of computer programs, may be sent and/orreceived through internal communication modules 440. In another example,information received though internal communication modules 440 may bestored in memory units 210, in shared memory units 410, and so forth. Inan additional example, information retrieved from memory units 210and/or shared memory units 410 may be transmitted using internalcommunication modules 440. In another example, input data may betransmitted and/or received using internal communication modules 440.Examples of such input data may include input data inputted by a userusing user input devices.

In some embodiments, the one or more external communication modules 450may be configured to receive and/or to transmit information. Forexample, control signals may be sent and/or received through externalcommunication modules 450. In another example, information receivedthough external communication modules 450 may be stored in memory units210, in shared memory units 410, and so forth. In an additional example,information retrieved from memory units 210 and/or shared memory units410 may be transmitted using external communication modules 450. Inanother example, input data may be transmitted and/or received usingexternal communication modules 450. Examples of such input data mayinclude: input data inputted by a user using user input devices;information captured from the environment of computing device 200 usingone or more sensors; and so forth. Examples of such sensors may include:audio sensors 250; image sensors 260; motion sensors 270; positioningsensors 275; chemical sensors; temperature sensors; barometers; and soforth.

FIG. 6 is a block diagrams illustrating a possible ecosystem 600. Inthis example, the ecosystem may comprise one or more patients 610, oneor more relatives 620, one or more medical care providers 630, one ormore insurers 640, one or more regulators 650, one or more researchers660, and one or more facilitators 670. In some embodiments, otherecosystems may exist. In some examples, ecosystem 600 may comprise oneor more additional entities, while some of the entities listed above maybe excluded from ecosystem 600. For example, patients 610 and/orrelatives 620 and/or medical care providers 630 and/or insurers 640and/or regulators 650 and/or researchers 660 and/or facilitators 670 maybe excluded from ecosystem 600. For example, ecosystem 600 may furtherinclude financial institutes (such as banks, credit companies, etc.),legal firms, non-medical service providers, research institutes, and soforth.

In some embodiments, entities of ecosystem 600 (such as patients 610,relatives 620, medical care providers 630, insurers 640, regulators 650,researcher 660, facilitators 670, etc.) may use computerized devices(such as computing device 200, computational node 500, cloud platform400, etc.) to perform part and/or all of their functions and/or duties.For example, the entities may use computerized devices to store and/oraccess and/or process data (some examples of such data may includemedical records 702, scheduling records 704, financial records 706,insurance records 708, research records 710, indexes 712, identifiers714, and/or permissions 716 described below), to communicate (forexample over communication network 130), and so forth.

In some embodiments, patients 610 may comprise one or more individualsthat received and/or are about to receive medical care.

In some embodiments, relatives 620 may comprise one or more individualsthat have some bearing on the medical care of at least one patient 610.For example, relatives 620 may comprise one or more of a family memberof patient 610, a friend of patient 610, a legal guardian of patient610, a next of kin of patient 610, a non-medical care giver of patient610, and so forth.

In some embodiments, medical care providers 630 may comprise one or moreindividual and/or one or more institutes that provides (in the pastand/or present and/or future) medical care to patients 610. For example,medical care providers 630 may comprise one or more medical careprofessionals (such as medical doctors, nurses, therapists, stuff ofmedical care institutes, etc.), one or more medical care institutes(such as hospitals, clinics, labs, etc.), and so forth.

In some embodiments, insurers 640 may comprise one or more individualsand/or one or more institutes that cover medical expenses (such asmedical expenses of at least some of patients 610 and/or medical careproviders 630) and/or insures medical care providers 630 for malpracticecosts. In some examples, insurers may include insurance firms and/orgovernment agencies.

In some embodiments, regulators 650 may comprise official entitiesappointed to track and/or regulate the medical care provided to patents610 and/or the medical care provided by medical care providers 630and/or the medical care covered by insurers 640. For example, regulators650 may comprise government agencies (such as the FDA, NIH, EMA, CFDA,PMDA, etc.), professional associations (such as the WMA, AMA, EMA, CMA,JMA, etc.), court appointed oversight, and so forth.

In some embodiments, researcher 660 may comprise research personals,research facilities, research institutes, research teams (such as teams140A, 140B and 140C), and so forth. For example, researchers 660 maycomprise a university, a drug development company, a research professor,and so forth.

In some embodiments, facilitators 670 may comprise individuals and/orentities that facilitate the communication among entities of ecosystem600.

FIG. 7A illustrates an exemplary embodiment of memory 700 storing aplurality of modules. In some examples, memory 700 may be separate fromand/or integrated with memory units 210, separate from and/or integratedwith memory units 410, and so forth. In some examples, memory 700 may beincluded in a single device, for example in computing device 200, incloud platform 400, in computational node 500, and so forth. In someexamples, memory 700 may be distributed across several devices. Memory700 may store more or fewer modules than those shown in FIG. 7A. In thisexample, memory 700 may comprise: medical records 702, schedulingrecords 704, financial records 706, insurance records 708, researchrecords 710, indexes 712, identifiers 714, and permissions 716.

In some embodiments, at least part of medical records 702 schedulingrecords 704, financial records 706, insurance records 708, researchrecords 710, indexes 712, identifiers 714, and/or permissions 716 may bestored in a public database, in a public ledger, in a blockchain, in acomputerized devices (such as computing device 200, cloud platform 400,computational node 500, etc.), in a storage devices (such as remotestorage, network attached storage, etc.), and so forth. In someexamples, medical records 702, scheduling records 704, financial records706, insurance records 708, research records 710, indexes 712,identifiers 714, and/or permissions 716 may be stored in single databaseand/or blockchain and/or site and/or device, while in other examplesmedical records 702 may be distributed among a number of databasesand/or blockchains and/or sites and/or devices. In some examples,medical records of a single entity (such as patient 610, relative 620,medical care provider 630, insurer 640, regulator 650, researcher 660,facilitator 670, etc.) may be stored in single database and/orblockchain and/or site and/or device, while in other examples themedical records of the entity may be distributed among a number ofdatabases and/or blockchains and/or sites and/or devices.

In some embodiments, medical records 702 may comprise medical records ofone or more patients 610, medical records created and/or used by one ormore medical care providers 630, medical records associated with one ormore patients and/or medical care providers insured by one or moreinsurers 640, medical records surveyed by one or more regulators 650,medical records studied by one or more researchers 660, and so forth. Insome examples, medical records 702 may comprise medical information,such as information regarding medical conditions, medical care, medicaltreatment, EHR, genome data, and so forth.

In some embodiments, scheduling records 704 may comprise schedulingrelated information associated with patients 610 and/or relatives 620and/or medical care providers 630 and/or insurers 640 and/or regulators650 and/or researchers 660 and/or facilitators 670. The schedulingrelated information may relate to past and/or present and/or futureevents. For example, scheduling records 704 may comprise time and dateinformation for an appointment, for a lab test, for a medical exam, fora medical checkup, for a reminder related to medical care, and so forth.

In some embodiments, financial records 706 may comprise financialinformation associated with patients 610 and/or relatives 620 and/ormedical care providers 630 and/or insurers 640 and/or regulators 650and/or researchers 660 and/or facilitators 670. The financialinformation may relate to past, present, and/or future budget, costs,bills, coverage obligations, and/or payments associated with medicalcare.

In some embodiments, insurance records 708 may comprise insuranceinformation associated with patients 610 and/or relatives 620 and/ormedical care providers 630 and/or insurers 640 and/or regulators 650and/or researchers 660 and/or facilitators 670. The insuranceinformation may include past, present and/or future coverageinformation, insurance claims, insurance payments, and so forth,associated with medical care.

In some embodiments, research records 710 may comprise research recordsassociated with patients 610 and/or relatives 620 and/or medical careproviders 630 and/or insurers 640 and/or regulators 650 and/orresearchers 660 and/or facilitators 670. For example, research records710 may comprise research records compiled and/or studied by aresearcher 660. For example, research records 710 may comprise researchrecords pertaining to one or more medical researches.

In some embodiments, indexes 712 may comprise one or more partial and/orcomplete indexes. In some examples, an index may link an identifier of arecord (such as medical record 702 scheduling record 704, financialrecord 706, insurance record 708, research record 710, index 712,identifiers 714, permission 716, etc.) to entities (such as patients610, relatives 620, medical care providers 630, insurers 640, regulators650, researchers 660, facilitators 670, and so forth). For example, anindex may link an identifier of a record to the entity created therecord, to entities that accessed and/or edited the record, to entitiesthat has permission to access and/or edit the record, and so forth.

In some examples, an index may link an identifier of an entity (such aspatient 610, relative 620, medical care provider 630, insurer 640,regulator 650, researcher 660, facilitator 670, etc.) to records (suchas medical records 702 scheduling records 704, financial records 706,insurance records 708, research records 710, indexes 712, identifiers714, permissions 716, etc.) associated with the entity, to records thatthe entity has permissions to access and/or edit, to records accessedand/or edited by the entity, and so forth.

In some examples, an index may link an identifier of an entity (such aspatient 610, relative 620, medical care provider 630, insurer 640,regulator 650, researcher 660, facilitator 670, etc.) to computerizeddevices (such as computing device 200, computational node 500, cloudplatform 400, etc.) and/or to storage devices (such as remote storage,network attached storage, etc.) and/or to blockchains and/or todatabases containing information and/or at least part of the records(such as medical records 702 scheduling records 704, financial records706, insurance records 708, research records 710, indexes 712,identifiers 714, permissions 716, etc.) associated with the entity.

In some examples, an index may link an identifier of an entity (such aspatient 610, relative 620, medical care provider 630, insurer 640,regulator 650, researcher 660, facilitator 670, etc.) to other entities(such as patient 610, relative 620, medical care provider 630, insurer640, regulator 650, researcher 660, facilitator 670, and so forth). Forexample, an index may link an identifier of a patient 610 to relatives620 related to the patient, medical care providers 630 that createdand/or hold at least part of the records associated with the patient, tomedical care providers 630 that hold permissions to access and/or editat least parts of the records associated with the patient, to medicalcare providers 630 that accessed and/or edited at least parts of therecords associated with the patient, to one or more insurers 640associated with the patient, to regulators 650 dealing with medicalinformation related to the patient, to researchers 660 studying medicalinformation related to the patient, and so forth. For example, an indexmay link an identifier of a relative 620 to a patient 610 related to therelative and/or to entities associated with that patient. For example,an index may link an identifier of a medical care provider 630 topatients 610 and/or relatives of patient 620 that the medical careprovider treat and/or has permissions to access and/or edit at leastpart of their medical records, to other medical care providers 630 thatwork in conjunction with the medical care provider, to insurer 640 ofthe medical care provider and/or patients of the medical care providerand/or employees of the medical care provider and/or suppliers of themedical care provider, to regulators 650 supervising the medical careprovider, to researchers 660 working with and/or for the medical careprovider, and so forth. For example, an index may link an identifier ofan insurer 640 to entities insured by the insurer. For example, an indexmay link an identifier of a regulator 650 to entities supervised by theregulator. For example, an index may link an identifier of a researcher660 to entities studied by the researcher, to entities working inconjunction with the researcher, and so forth. For example, an index maylink an identifier of a facilitator 670 to entities recognized by and/orassociated with the facilitator.

In some embodiments, identifiers 714 may comprise identifiers ofentities (such as patients 610, relatives 620, medical care providers630, insurers 640, regulators 650, researchers 660, facilitators 670,etc.) and/or records (such as medical records 702 scheduling records704, financial records 706, insurance records 708, research records 710,indexes 712, permissions 716, and so forth). In some examples,identifier of an entity and/or record may be unique, while in otherexamples more than one identifier may identify the same entity and/orrecord.

In some embodiments, permissions 716 may specify which entities (such aspatients 610, relatives 620, medical care providers 630, insurers 640,regulators 650, researchers 660, facilitators 670, etc.) may createand/or edit and/or access which records (such as medical records 702scheduling records 704, financial records 706, insurance records 708,research records 710, indexes 712, identifiers 714, permissions 716, andso forth). For example, permissions 716 may comprise a group or entitiesallowed to create and/or edit and/or access selected records, a group orentities prohibited from creating and/or editing and/or accessingselected records, and so forth. For example, the selected records abovemay be specified as a group of records, as a rule defining a group ofrecords, as the records associated with selected entities, and so forth.In some examples, permissions 716 may further specify which entities areallowed to grant which permission to which other entities regardingwhich records.

In some embodiments, medical care providers 630 may create and/or editrecords of a patient 610 (such as medical records 702 scheduling records704, financial records 706, insurance records 708, research records 710,indexes 712, identifiers 714, permissions 716, and so forth). Theserecords may be indexed in indexes 712. These records may be accessedand/or edited by the patient, by some relatives 620 of the patient, byother medical care providers 630 treating the patient, by insurers 640of the patient and/or the medical care provider, and so forth.

In some embodiments, an insurer 640 may access and/or edit and/or createrecords (such as medical records 702 scheduling records 704, financialrecords 706, insurance records 708, research records 710, indexes 712,identifiers 714, permissions 716, etc.) of entities (such as patients610, relatives 620, medical care providers 630, other insurers 640,regulators 650, researchers 660, facilitators 670, etc.) insured by theinsurer.

In some embodiments, a regulator 650 may access and/or edit and/orcreate records (such as medical records 702 scheduling records 704,financial records 706, insurance records 708, research records 710,indexes 712, identifiers 714, permissions 716, etc.) of entities (suchas patients 610, relatives 620, medical care providers 630, otherinsurers 640, regulators 650, researchers 660, facilitators 670, etc.)supervised by the regulator.

In some embodiments, a researcher 660 may access and/or edit and/orcreate records (such as medical records 702 scheduling records 704,financial records 706, insurance records 708, research records 710,indexes 712, identifiers 714, permissions 716, etc.) of entities (suchas patients 610, relatives 620, medical care providers 630, otherinsurers 640, regulators 650, researchers 660, facilitators 670, etc.)studied by the researcher.

In some embodiments, a facilitator 670 may access and/or edit and/orcreate records (such as medical records 702 scheduling records 704,financial records 706, insurance records 708, research records 710,indexes 712, identifiers 714, permissions 716, etc.) of entities (suchas patients 610, relatives 620, medical care providers 630, otherinsurers 640, regulators 650, researchers 660, facilitators 670, and soforth). For example, a facilitator 670 may create an identifier 714, mayverify an identity 714, and so forth. For example, a facilitator 670 mayprovide permission to relatives 620 of a patient 610 to access thepatient's record after the death of the patient by editing permissions716. For example, a facilitator 670 may recognize an entity as alicensed medical professional, as a licensed insurer, as a legitimateresearcher, as a legal regulator, and so forth.

In some embodiments, access to a record and/or creation of a recordand/or edition to a record may be recorded, for example in a log, inindexes 712, in the accessed and/or created and/or edited record, and soforth.

FIG. 7B illustrates an exemplary embodiment of a data element 730. Insome examples, data element 730 may be stored in a memory unit (such asmemory 700), in a database, in a data structure, in a table, and soforth. In some examples, data element 730 may comprise one or more datafields. For example, in the example of FIG. 7B data element 730 maycomprise patient name 732, patient address 734, patient age 736,physician name 738 and medication data 740. In some examples, dataelement 730 may include portion 742 that includes identifiableinformation of a patient (in this example, portion 742 may include datafields patient name 732 and patient address 734), and portion 744 thatdoes not include identifiable information of a patient (in this example,portion 744 may include data fields patient age 736, physician name 738and medication data 740). In some examples, data element 730 may includeadditional portions and/or data fields that are not included in portion742 and portion 744.

FIG. 7C illustrates an exemplary embodiment of a data element 750. Insome examples, data element 750 may be stored in a memory unit (such asmemory 700), in a database, in a data structure, in a table, and soforth. In some examples, data element 750 may comprise a table includingone or more columns. For example, in the example of FIG. 7C data element750 may comprise patient name column 752, patient phone number column754, physician name 756 and appointment time 758. In some examples, dataelement 750 may include portions that includes identifiable informationof a patient and portions that does not include identifiable informationof a patient. The division of data element 750 to portions that includesidentifiable information of a patient and portions that does not includeidentifiable information of a patient may be based on the type of thecolumn (for example, patient name column 752 and patient phone numbercolumn 754 may be identified as including identifiable information of apatient based on the type of the column), may be based on a distributionof values in the column (for example, in clinics that only have one orfew appointments at the same time, appointment time column 758 may beidentified as including identifiable information of a patient based onthe distribution of appointment times in the column, while in clinicsthat have many appointments at the same time, appointment time column758 may be identified as not including identifiable information of apatient based on the distribution of appointment times in the column),may be based on values in the column (for example, a comparison ofvalues in patient name column 752 with a dictionary may indicates thatthe values include names, and therefore patient name column 752 beidentified as including identifiable information of a patient), may bebased on analysis of values in the column and/or in other columns (forexample using a machine learning model trained using training examples),and so forth.

In some embodiments, an identified copy of the data element and ade-identified copy of the data element may be used, for example asdescribed below. For example, the identified copy of the data elementmay include identified information of a patient, and the de-identifiedcopy of the data element may include no identified information of apatient. In the examples of FIG. 7B and FIG. 7C, any one of data element730 and data element 750 includes identified information of a patient,and therefore data element 730 and data element 750 are identifiedcopies. In one example, de-identified copies of data element 730 anddata element 750 may include data fields that comprise no identifiedinformation of a patient and may exclude fields that comprise identifiedinformation of a patient. For example, a de-identified copy of dataelement 730 may include portion 744 of data element 730 and may notinclude any part of portion 742 of data element 730. In another example,a de-identified copy of data element 730 may include data field 756 butnot data fields 752 and 754, and may include or not include data field758 based on whether data field 758 includes identified information of apatient (for example, as determined as described above). In one example,the identified copy and/or the de-identified copy may be obtained byaccessing the identified copy and/or the de-identified copy in a memory(such as memory units 210, shared memory modules 410, and so forth). Inanother example, the identified copy and/or the de-identified copy maybe received from an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, etc.),may be received from a user, and so forth. In some examples, theidentified copy may be analyzed to generate the de-identified copy. Forexample, a machine learning model may be trained using training exampleto generate de-identified copies of data sources that includesidentified information, and the trained machine learning model may beused to analyze a data element and generate a de-identified copy of dataelement. An example of such training example may include a sample dataelement together with a desired de-identified copy of the sample dataelement. In another example, the identified copy may include a pluralityof fields, for each field it may be determined whether the fieldincludes identified information of a patient (for example as describedabove), and fields that are determined to not include identifiedinformation of a patient may be included in the de-identified copy,while fields that are determined to include identified information of apatient may be omitted from the de-identified copy.

In some embodiments, a method, such as methods 800, 820, 830, 840, 860,880, 900, 920, 930, 940, 950, 960, 1000, 1100, 1200, 1300, etc., maycomprise of one or more steps. In some examples, a method, as well asall individual steps therein, may be performed by various aspects ofcomputing device 200, cloud platform 400, computational node 500, and soforth. For example, the method may be performed by processing units 220executing software instructions stored within memory units 210 and/orwithin shared memory modules 410. In some examples, a method, as well asall individual steps therein, may be performed by a dedicated hardware.In some examples, computer readable medium (such as a non-transitorycomputer readable medium) may store data and/or computer implementableinstructions for carrying out a method. Some examples of possibleexecution manners of a method may include continuous execution (forexample, returning to the beginning of the method once the method normalexecution ends), periodically execution, executing the method atselected times, execution upon the detection of a trigger (some examplesof such trigger may include a trigger from a user, a trigger fromanother method, a trigger from an external device, etc.), and so forth.

In some embodiments, machine learning algorithms (also referred to asmachine learning models in the present disclosure) may be trained usingtraining examples, for example in the cases described below. Someexamples of such machine learning algorithms may include classificationalgorithms, data regressions algorithms, image segmentation algorithms,visual detection algorithms (such as object detectors, face detectors,person detectors, motion detectors, edge detectors, etc.), visualrecognition algorithms (such as face recognition, person recognition,object recognition, etc.), speech recognition algorithms, mathematicalembedding algorithms, natural language processing algorithms, supportvector machines, random forests, nearest neighbors algorithms, deeplearning algorithms, artificial neural network algorithms, convolutionalneural network algorithms, recursive neural network algorithms, linearalgorithms, non-linear algorithms, ensemble algorithms, and so forth.For example, a trained machine learning algorithm may comprise aninference model, such as a predictive model, a classification model, aregression model, a clustering model, a segmentation model, anartificial neural network (such as a deep neural network, aconvolutional neural network, a recursive neural network, etc.), arandom forest, a support vector machine, and so forth. In some examples,the training examples may include example inputs together with thedesired outputs corresponding to the example inputs. Further, in someexamples, training machine learning algorithms using the trainingexamples may generate a trained machine learning algorithm, and thetrained machine learning algorithm may be used to estimate outputs forinputs not included in the training examples. In some examples,engineers, scientists, processes and machines that train machinelearning algorithms may further use validation examples and/or testexamples. For example, validation examples and/or test examples mayinclude example inputs together with the desired outputs correspondingto the example inputs, a trained machine learning algorithm and/or anintermediately trained machine learning algorithm may be used toestimate outputs for the example inputs of the validation examplesand/or test examples, the estimated outputs may be compared to thecorresponding desired outputs, and the trained machine learningalgorithm and/or the intermediately trained machine learning algorithmmay be evaluated based on a result of the comparison. In some examples,a machine learning algorithm may have parameters and hyper parameters,where the hyper parameters are set manually by a person or automaticallyby an process external to the machine learning algorithm (such as ahyper parameter search algorithm), and the parameters of the machinelearning algorithm are set by the machine learning algorithm accordingto the training examples. In some implementations, the hyper-parametersare set according to the training examples and the validation examples,and the parameters are set according to the training examples and theselected hyper-parameters.

In some embodiments, trained machine learning algorithms (also referredto as trained machine learning models in the present disclosure) may beused to analyze inputs and generate outputs, for example in the casesdescribed below. In some examples, a trained machine learning algorithmmay be used as an inference model that when provided with an inputgenerates an inferred output. For example, a trained machine learningalgorithm may include a classification algorithm, the input may includea sample, and the inferred output may include a classification of thesample (such as an inferred label, an inferred tag, and so forth). Inanother example, a trained machine learning algorithm may include aregression model, the input may include a sample, and the inferredoutput may include an inferred value for the sample. In yet anotherexample, a trained machine learning algorithm may include a clusteringmodel, the input may include a sample, and the inferred output mayinclude an assignment of the sample to at least one cluster. In anadditional example, a trained machine learning algorithm may include aclassification algorithm, the input may include an image, and theinferred output may include a classification of an item depicted in theimage. In yet another example, a trained machine learning algorithm mayinclude a regression model, the input may include an image, and theinferred output may include an inferred value for an item depicted inthe image (such as an estimated property of the item, such as size,volume, age of a person depicted in the image, cost of a productdepicted in the image, and so forth). In an additional example, atrained machine learning algorithm may include an image segmentationmodel, the input may include an image, and the inferred output mayinclude a segmentation of the image. In yet another example, a trainedmachine learning algorithm may include an object detector, the input mayinclude an image, and the inferred output may include one or moredetected objects in the image and/or one or more locations of objectswithin the image. In some examples, the trained machine learningalgorithm may include one or more formulas and/or one or more functionsand/or one or more rules and/or one or more procedures, the input may beused as input to the formulas and/or functions and/or rules and/orprocedures, and the inferred output may be based on the outputs of theformulas and/or functions and/or rules and/or procedures (for example,selecting one of the outputs of the formulas and/or functions and/orrules and/or procedures, using a statistical measure of the outputs ofthe formulas and/or functions and/or rules and/or procedures, and soforth).

In some embodiments, artificial neural networks may be configured toanalyze inputs and generate corresponding outputs. Some examples of suchartificial neural networks may comprise shallow artificial neuralnetworks, deep artificial neural networks, feedback artificial neuralnetworks, feed forward artificial neural networks, autoencoderartificial neural networks, probabilistic artificial neural networks,time delay artificial neural networks, convolutional artificial neuralnetworks, recurrent artificial neural networks, long short term memoryartificial neural networks, and so forth. In some examples, anartificial neural network may be configured manually. For example, astructure of the artificial neural network may be selected manually, atype of an artificial neuron of the artificial neural network may beselected manually, a parameter of the artificial neural network (such asa parameter of an artificial neuron of the artificial neural network)may be selected manually, and so forth. In some examples, an artificialneural network may be configured using a machine learning algorithm. Forexample, a user may select hyper-parameters for the an artificial neuralnetwork and/or the machine learning algorithm, and the machine learningalgorithm may use the hyper-parameters and training examples todetermine the parameters of the artificial neural network, for exampleusing back propagation, using gradient descent, using stochasticgradient descent, using mini-batch gradient descent, and so forth. Insome examples, an artificial neural network may be created from two ormore other artificial neural networks by combining the two or more otherartificial neural networks into a single artificial neural network.

FIG. 8A illustrates an example of a method 800 for enabling graphicalillustration based on private medical information. In this example,method 800 may comprise: receiving a subgroup defining input (Step 802),the subgroup defining input may define a subgroup of a group ofpatients; receiving a statistical query about the subgroup of the groupof patients (Step 804); determine a size of the subgroup of the group ofpatients (Step 806); comparing the determined size of the subgroup ofthe group of patients with a selected subgroup size threshold (Step808); in response to the determined size of the subgroup of the group ofpatients being larger than the selected subgroup size threshold,providing first information (Step 810), the first information may bebased on the statistical query and configured to enable a presentationof a graphical illustration of an estimated property of the subgroup ofthe group of patients; and in response to the determined size of thesubgroup of the group of patients being smaller than the selectedsubgroup size threshold, forgoing providing the first information (Step812). In some implementations, method 800 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. For example, in some cases Step 802 and/or Step 804 and/orStep 806 and/or Step 808 and/or Step 810 and/or Step 812 may be excludedfrom method 800. In some implementations, one or more steps illustratedin FIG. 8A may be executed in a different order and/or one or moregroups of steps may be executed simultaneously and/or a plurality ofsteps may be combined into single step and/or a single step may bebroken down to a plurality of steps. In some examples, after completionof Step 810 and/or Step 812, method 800 may continue to execute method840 and/or method 860. In some examples, after completion of Step 810,method 800 may continue to execute method 880. In some examples, aftercompletion of Step 812, method 800 may continue to execute method 830.

In some embodiments, Step 802 may comprise receiving a subgroup defininginput, where the subgroup defining input may define a subgroup of agroup of patients. In some examples, the subgroup defining input may bebased on a first input from a user, such as user 146. For example, Step802 may read at least part of the subgroup defining input from memory(such as memory units 210, shared memory modules 410, and so forth). Inanother example, Step 802 may receive at least part of the subgroupdefining input from an external device (such as computerized dataanalysis device 144, an external device associated with the user, suchas a workstation, a mobile device of the user, etc.) over acommunication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, etc.), may receive at least part of thesubgroup defining input from the user (for example, through a userinterface, through a web page, using an input device, throughcomputerized data analysis device 144, etc.), and so forth. In oneexample, the subgroup defining input received by Step 802 may include aquery (for example, in a query language such as Structured QueryLanguage) that selects patients of the group of patients to the subgroupof a group of patients. In another example, the subgroup defining inputreceived by Step 802 may include one or more criterions that dictatewhich patients of the group of patients are included in the subgroup ofa group of patients. Some examples on such criterions may includerestrictions on the age of the patients (for example, ‘patients betweenthe ages of 30 and 40’), restrictions on the gender of the patients (forexample, ‘female patients’), restrictions on physical characteristics ofthe patients (for example, ‘patients with Body Mass Index under 20’),restriction on ethnicity of the patients (for example, ‘Alaskan nativepatients’), restrictions on demographics of the patients (for example,‘patients with at least two children’, ‘patients with twelve or moreyears of education’, ‘patients with an annual income of at least$30,000’, etc.), restrictions on symptoms experienced by the patients(for example, ‘patients suffering from back pain’), restrictions on themedical condition of the patients (for example, ‘patients suffering frompsychiatric disorders’), restriction on durations of medical conditionsof the patients (for example, ‘patients suffering from fibromyalgia forover a year’), restrictions on the medical treatment received by thepatients (for example, ‘patients received neuroleptic medication’,‘patients who underwent corrective laser eye surgery’, etc.),restrictions on adverse effects reported by the patients, restrictionson the medical examinations performed on the patients (for example,‘patients who underwent colonoscopy in the last year’), restrictions ona medical outcome associated with the patients (for example, ‘patientswith at least one hospital readmission’), restrictions on caregivers ofthe patients (for example, ‘patients treated in a community clinic’),and so forth.

In some embodiments, Step 804 may comprise receiving a statistical queryabout a subgroup of the group of patients, for example receiving astatistical query about the subgroup of the group of patients defined bythe subgroup defining input of Step 802. In some examples, thestatistical query may be based on an input from a user (for example,based on a second input from the user of Step 802, based on an inputfrom user 146, based on an input from user 147, and so forth). Forexample, Step 804 may read at least part of the statistical query aboutthe subgroup of the group of patients from memory (such as memory units210, shared memory modules 410, and so forth). In another example, Step804 may receive at least part of the statistical query about thesubgroup of the group of patients from an external device (such ascomputerized data analysis device 144, an external device associatedwith the user, such as a workstation, a mobile device of the user, etc.)over a communication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, etc.), may receive at least part of thestatistical query about the subgroup of the group of patients from theuser (for example, through a user interface, through a web page, usingan input device, through computerized data analysis device 144, etc.),and so forth. In one example, the statistical query about the subgroupof the group of patients received by Step 804 may include a query in aquery language (such as Structured Query Language) that expresses astatistical query about the subgroup of the group of patients. Inanother example, the statistical query about the subgroup of the groupof patients received by Step 804 may include one or more mathematicalformulas for calculating a statistical measure of the subgroup of thegroup of patients.

In some embodiments, Step 806 may comprise determining a size of asubgroup of a group of patients, for example determining a size of thesubgroup of the group of patients defined by the subgroup defining inputof Step 802, or determining a size of the updated subgroup of the groupof patients of method 840 (described below). For example, Step 806 maydetermine an exact size of the subgroup of the group of patients, Step806 may determine an estimated size of the subgroup of the group ofpatients, and so forth. In one example, Step 806 may count the patientsin the subgroup of the group of patients, for example by determining foreach patient in the group of patients whether that patient is in thesubgroup of the group of patients using the subgroup defining input ofStep 802, and the size of the subgroup of the group of patientsdetermined by Step 806 may be the determined number of patients in thesubgroup of the group of patients. In another example, Step 806 mayestimate the size of the subgroup of the group of patients, for exampleby determining for each patient in a selected sample of the group ofpatients whether that patient is in the subgroup of the group ofpatients using the subgroup defining input of Step 802 to estimate theratio of patients in the subgroup of the group of patients from theentire group of patients, and using the estimated ratio and a known sizeof the group of patients to estimate the size of the subgroup of thegroup of patients. In some examples, a weight may be associated witheach patient in the group of patients, and Step 806 may determine atotal weight (exact total weight and/or estimated total weight) of thepatients in the subgroup of the group of patients, and the size of thesubgroup of the group of patients determined by Step 806 may be thedetermined total weight.

In some embodiments, Step 808 may comprise comparing a size of asubgroup of the group of patients (for example, the size determined byStep 806 of the subgroup of the group of patients defined by thesubgroup defining input of Step 802) with a selected subgroup sizethreshold. For example, Step 808 may read the subgroup size thresholdfrom memory (such as memory units 210, shared memory modules 410, and soforth). In another example, Step 808 may receive the subgroup sizethreshold from an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth). In yet another example, Step 808 may receive the subgroup sizethreshold from a user (for example, through a user interface, through aweb page, using an input device, and so forth). In an additionalexample, Step 808 may receive the subgroup size threshold as aconfiguration parameter, for example from a configuration file. In someexamples, Step 808 may randomly select the subgroup size threshold froma distribution of thresholds. Some examples of such distribution ofthresholds may include discrete distribution, continuous distribution,uniform distribution, binomial distribution, normal distribution,Bernoulli distribution, Poisson distribution, exponential distribution,Polya-Eggenberger distribution, zeta distribution, and so forth. In someexamples, Step 808 may select the subgroup size threshold based onwhether particular patients are included in the subgroup of the group ofpatients. For example, in response to the subgroup of the group ofpatients including a first patient, Step 808 may select a first valuefor the subgroup size threshold, and in response to the subgroup of thegroup of patients not including the first patient, Step 808 may select asecond value for the subgroup size threshold (the second value differsfrom the first value). In another example, in response to the subgroupof the group of patients including at least a selected number ofpatients of a first type, Step 808 may select a first value for thesubgroup size threshold, and in response to the subgroup of the group ofpatients not including at least the selected number of patients of thefirst type, Step 808 may select a second value for the subgroup sizethreshold (the second value differs from the first value). For example,the selected number of patients may be one, two, between three and five,above five, and so forth. In another example, Step 808 may select theselected number of patients based on at least one of the first type, thedetermined size of the subgroup of the group of patients, the size ofthe group of patients, and so forth. In some examples, Step 808 mayselect the subgroup size threshold based on a type of the property ofthe subgroup of the group of patients. For example, in response to theproperty of the subgroup of the group of patients being of a first type,Step 808 may select a first value for the subgroup size threshold, andin response to the property of the subgroup of the group of patientsbeing of a second type, Step 808 may select a second value for thesubgroup size threshold (the second value differs from the first value).In some examples, Step 808 may select the subgroup size threshold basedon the user of Step 802. For example, in response to a first user, Step808 may select a first value for the subgroup size threshold, and inresponse to a second user, Step 808 may select a second value for thesubgroup size threshold (the second value differs from the first value).In another example, Step 808 may select the subgroup size thresholdbased on at least one of an identity of the user, a type of the user, anidentity of a group of at least two users that includes the user, pastbehavior of the user, at least one previous statistical query that isbased on an input from the user, information provided in response to atleast one previous statistical query that is based on an input from theuser, and so forth. In some examples, Step 808 may select the subgroupsize threshold based on at least one previous statistical query aboutthe subgroup of the group of patients. For example, in response to theprevious statistical queries about the subgroup of the group of patientsincluding a first statistical query, Step 808 may select a first valuefor the subgroup size threshold, and in response to the previousstatistical queries about the subgroup of the group of patients notincluding the first statistical query, Step 808 may select a secondvalue for the subgroup size threshold (the second value differs from thefirst value). In another example, Step 808 may select the subgroup sizethreshold based on information provided in response to at least oneprevious statistical query about the subgroup of the group of patients.For example, in response to the information provided in response to atleast one previous statistical query having a first property, Step 808may select a first value for the subgroup size threshold, and inresponse to at least one previous statistical query not having the firstproperty, Step 808 may select a second value for the subgroup sizethreshold (the second value differs from the first value).

In some embodiments, Step 810 may comprise providing first information,for example in response to the size determined by Step 806 of thesubgroup of the group of patients defined by the subgroup defining inputof Step 802 being larger than the selected subgroup size threshold ofStep 808. In some examples, the first information may be based on astatistical query (for example, on the statistical query received byStep 804). In some examples, the first information may be configured toenable a presentation of a graphical illustration of an estimatedproperty of a subgroup of the group of patients (for example, of anestimated property of the subgroup of the group of patients defined bythe subgroup defining input of Step 802), for example in response to thesecond input from the user of Step 804. For example, Step 810 may storethe first information in memory (such as memory 700, memory units 210,memory modules 410, etc.), in storage device (such as local storage,remote storage, network attached storage, etc.), and so forth. Inanother example, Step 810 may transmit the first information to anexternal device, for example over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth). In some examples, Step 810 may present the first information toa user, for example through a user interface, through a web page, usingan output device (such as a display screen, an augmented reality displaysystem, a printer, a LED indicator, etc.), through computerized dataanalysis device 144, and so forth.

In some embodiments, Step 812 may comprise forgoing and/or withholdingproviding the first information of Step 810, for example in response tothe size determined by Step 806 of the subgroup of the group of patientsdefined by the subgroup defining input of Step 802 being smaller thanthe selected subgroup size threshold of Step 808.

In some examples, provided information (such as the first informationprovided by Step 810 or Step 834, the second information provided byStep 848, Step 868 or Step 888, the first estimated property of themedical data provided by Step 904, the second estimated property of themedical data provided by Step 912 or by Step 934 or by Step 952, and soforth) may be based on a statistical query (such as the statisticalquery received by Step 804, the second statistical query received byStep 884, the first statistical query received by Step 902, the secondstatistical query received by Step 906, and so forth). For example, thefirst information provided by Step 810 or Step 834 may be based on thestatistical query received by Step 804, the second information providedby Step 848 or Step 868 may be based on the statistical query receivedby Step 804, the second information provided by Step 888 may be based onthe second statistical query received by Step 884, the first estimatedproperty of the medical data provided by Step 904 may be based on thefirst statistical query received by Step 902, the second estimatedproperty of the medical data provided by Step 912 or by Step 934 or byStep 952 may be based on the second statistical query received by Step906, and so forth. In some examples, the provided information mayinclude information generated using the statistical query. For example,the statistical query may include a query in a query language (such asStructured Query Language), and the provided information may include aresult of applying the included query to a database and/or a datastructure, or include the result combined with some noise (for example,as generated using method 820 or using method 920). In another example,the statistical query may include a mathematical formula, and theprovided information may include a statistical measure of the subgroupof the group of patients and/or of the medical data calculated accordingto the included mathematical formula, or include the calculatedstatistical measure combined with some noise (for example, as generatedusing method 820 or using method 920). In yet another example,information in private medical data 112 and/or memory 700 may beanalyzed using the statistical query to determine the providedinformation.

In some examples, provided information (such as the first informationprovided by Step 810 or Step 834, the second information provided byStep 848, Step 868 or Step 888, etc.) may be configured to enable and/orcause a presentation of a graphical illustration of an estimatedproperty of a subgroup of the group of patients (for example, of anestimated property of the subgroup of the group of patients defined bythe subgroup defining input of Step 802, of an estimated property of thesubgroup of the group of patients defined by the second subgroupdefining input of Step 882, etc.), for example in response to an inputfrom the user (such as the second input from the user of Step 804, thefourth input from the user of Step 884, and so forth). For example, theestimated property of the subgroup of the group of patients may be anactual property of the subgroup of the group of patients. In anotherexample, the estimated property of the subgroup of the group of patientsmay be an approximation of an actual property of the subgroup of thegroup of patients. In some examples, the property of the subgroup of thegroup of patients to be graphically illustrated may be selected based onthe statistical query. For example, in response to a first statisticalquery, a first property of the subgroup of the group of patients may begraphically illustrated, and in response to a second statistical query,a second property of the subgroup of the group of patients may begraphically illustrated (the second property differs from the firstproperty). Some non-limiting examples of such properties of the subgroupof the group of patients may include a distribution of data associatedwith the patients in the subgroup of the group of patients, distributionof ages of patients in the subgroup of the group of patients,distribution of genders of patients in the subgroup of the group ofpatients, distribution of physical characteristics of patients in thesubgroup of the group of patients, distribution of ethnicity of patientsin the subgroup of the group of patients, distribution of demographicproperties of patients in the subgroup of the group of patients,distribution of symptoms experienced by patients in the subgroup of thegroup of patients, distribution of medical conditions of patients in thesubgroup of the group of patients, distribution of durations of medicalconditions of patients in the subgroup of the group of patients,distribution of medical treatment received by patients in the subgroupof the group of patients, distribution of adverse effects reported bypatients in the subgroup of the group of patients, distribution ofmedical outcome associated with patients in the subgroup of the group ofpatients, distribution of properties of caregivers of patients in thesubgroup of the group of patients, and so forth.

In some examples, the graphical illustration may include an image. Forexample, the image may be based on the images associated with thepatients of the subgroup of the group of patients. In some examples,each patient of the subgroup of the group of patients may be associatedwith a medical image, and the graphical illustration may include adisplay of an image based on the images associated with the patients ofthe subgroup of the group of patients. Some non-limiting examples ofsuch medical images may include x-rays, computed tomography images,ultrasound images, magnetic resonance images, positron-emissiontomography images, color images (for example, of a skin feature, of amedical operation, etc.), images of biopsies, and so forth. For example,the displayed image may be an average image of the images associatedwith the patients of the subgroup of the group of patients. In anotherexample, the displayed image may be a weighted sum of the imagesassociated with the patients of the subgroup of the group of patients.In yet another example, the displayed image may be a function of theimages associated with the patients of the subgroup of the group ofpatients. In an additional example, at least one pixel of the displayedimage may be a weighted sum of a group of pixels, and the group ofpixels may comprise at least one pixel from each image of the imagesassociated with the patients of the subgroup of the group of patients.In yet another example, at least one pixel of the displayed image may bea function of a group of pixels, the group of pixels may comprise atleast one pixel from each image of the images associated with thepatients of the subgroup of the group of patients.

In some examples, the graphical illustration may include a visualizationof a correlation matrix. In one example, the graphical illustration mayinclude a visualization of a correlation matrix between survivalprobabilities of patients of the subgroup of the group of patients andtreatment choices associated with the patients of the subgroup of thegroup of patients. In another example, the graphical illustration mayinclude a visualization of a correlation matrix between outcomeassociated with patients of the subgroup of the group of patients anddemographic characteristics of the patients of the subgroup of the groupof patients. In some examples, the graphical illustration may include avisualization of a correlation tensor. For example, the graphicalillustration may include a visualization of a correlation tensorvisualizing relations among data elements associated with patients ofthe subgroup of the group of patients.

In some examples, the graphical illustration may include a visualizationof correlations between data elements associated with patients of thesubgroup of the group of patients. For example, the visualization of thecorrelations between the data elements may include one or more charts.Some non-limiting examples of such charts may include a bar chart, aplot chart, a line chart, an area chart, a scatter plot, a bubble chart,a column chart, a surface chart, a radar chart, a combo chart, and soforth.

In some examples, the graphical illustration may include a visualizationof a distribution of patients with respect to a characteristic of thepatients. Some non-limiting examples of such charts may include a piechart, a donut chart, a histogram, and so forth.

FIG. 8B illustrates an example of a method 820 for determining anestimated property of a subgroup of a group of patients. In thisexample, method 820 may comprise: obtaining a privacy parameter (Step822); selecting at least one noise value based on the obtained privacyparameter (Step 824); and combining the at least one noise value with anactual property of a subgroup of a group of patients to determine theestimated property of the subgroup of the group of patients (Step 826).In some implementations, method 820 may comprise one or more additionalsteps, while some of the steps listed above may be modified or excluded.For example, in some cases Step 822 and/or Step 824 and/or Step 826 maybe excluded from method 820. In some implementations, one or more stepsillustrated in FIG. 8B may be executed in a different order and/or oneor more groups of steps may be executed simultaneously and/or aplurality of steps may be combined into single step and/or a single stepmay be broken down to a plurality of steps.

In some embodiments, Step 822 may comprise obtaining a privacyparameter. For example, the obtained privacy parameter may comprise atleast one of a number, a seed to a pseudo-random number generator, aselection of a noise model, a type of noise distribution, a parameter ofa noise distribution, an amount of randomness, and so forth. Forexample, Step 822 may read at least part of the privacy parameter frommemory (such as memory units 210, shared memory modules 410, and soforth). In another example, Step 822 may receive at least part of theprivacy parameter from an external device (such as computerized dataanalysis device 144, an external device associated with the user, suchas a workstation, a mobile device of the user, etc.) over acommunication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, and so forth). In yet another example, Step822 may receive at least part of the privacy parameter from a user (forexample, through a user interface, through a web page, using an inputdevice, and so forth). In an additional example, Step 822 may receive atleast part of the privacy parameter as a configuration parameter, forexample from a configuration file. In some examples, Step 822 mayrandomly select the privacy parameter from a distribution of privacyparameters. Some examples of such distribution of privacy parameters mayinclude discrete distribution, continuous distribution, uniformdistribution, binomial distribution, normal distribution, Bernoullidistribution, Poisson distribution, exponential distribution,Polya-Eggenberger distribution, zeta distribution, and so forth. In someexamples, Step 822 may select the privacy parameter based on a size ofthe subgroup of the group of patients, for example based on the size ofthe subgroup of the group of patients determined by Step 806. Forexample, in response to a first determined size of the subgroup of thegroup of patients, Step 822 may select a first value for the privacyparameter, and in response to a second determined size of the subgroupof the group of patients, Step 822 may select a second value for theprivacy parameter (the second value differs from the first value). Insome examples, Step 822 may select the privacy parameter based onwhether particular patients are included in the subgroup of the group ofpatients. For example, in response to the subgroup of the group ofpatients including a first patient, Step 822 may select a first valuefor the privacy parameter, and in response to the subgroup of the groupof patients not including the first patient, Step 822 may select asecond value for the privacy parameter (the second value differs fromthe first value). In another example, in response to the subgroup of thegroup of patients including at least a selected number of patients of afirst type, Step 822 may select a first value for the privacy parameter,and in response to the subgroup of the group of patients not includingat least the selected number of patients of the first type, Step 822 mayselect a second value for the privacy parameter (the second valuediffers from the first value). For example, the selected number ofpatients may be one, two, between three and five, above five, and soforth. In another example, Step 822 may select the selected number ofpatients based on at least one of the first type, the determined size ofthe subgroup of the group of patients, the size of the group ofpatients, and so forth. In some examples, Step 822 may select theprivacy parameter based on a type of the property of the subgroup of thegroup of patients. For example, in response to the property of thesubgroup of the group of patients being of a first type, Step 822 mayselect a first value for the privacy parameter, and in response to theproperty of the subgroup of the group of patients being of a secondtype, Step 822 may select a second value for the privacy parameter (thesecond value differs from the first value). In some examples, Step 822may select the privacy parameter based on the user of Step 802. Forexample, in response to a first user, Step 822 may select a first valuefor the privacy parameter, and in response to a second user, Step 822may select a second value for the privacy parameter (the second valuediffers from the first value). In another example, Step 822 may selectthe privacy parameter based on at least one of an identity of the user,a type of the user, an identity of a group of at least two users thatincludes the user, past behavior of the user, at least one previousstatistical query that is based on an input from the user, informationprovided in response to at least one previous statistical query that isbased on an input from the user, and so forth. In some examples, Step822 may select the privacy parameter based on at least one previousstatistical query about the subgroup of the group of patients. Forexample, in response to the previous statistical queries about thesubgroup of the group of patients including a first statistical query,Step 822 may select a first value for the privacy parameter, and inresponse to the previous statistical queries about the subgroup of thegroup of patients not including the first statistical query, Step 822may select a second value for the privacy parameter (the second valuediffers from the first value). In another example, Step 822 may selectthe privacy parameter based on information provided in response to atleast one previous statistical query about the subgroup of the group ofpatients. For example, in response to the information provided inresponse to at least one previous statistical query having a firstproperty, Step 822 may select a first value for the privacy parameter,and in response to at least one previous statistical query not havingthe first property, Step 822 may select a second value for the privacyparameter (the second value differs from the first value).

In some embodiments, Step 824 may comprise selecting at least one noisevalue based on the privacy parameter obtained by Step 822. For example,Step 824 may obtain one or more noise values from a pseudo-random numbergenerator initialized based on the privacy parameter obtained by Step822 (for example, initialized using a seed included in the privacyparameter). In another example, Step 824 may obtain one or more noisevalues from a noise model selected from a plurality of alternative noisemodels based on the privacy parameter obtained by Step 822 (for example,according to an indication of a selected noise model included in theprivacy parameter). In yet another example, Step 824 may obtain one ormore noise values from a noise distribution, and the type and/or atleast part of parameters of the noise distribution may be selected basedon the privacy parameter obtained by Step 822 (for example, according toan indication of a type of a noise distribution included in the privacyparameter, according to a parameter of the noise distribution includedin the privacy parameter, and so forth). In an additional example, Step824 may obtain one or more noise values from a list of previouslygenerated random values (for example, the selection of the one or morenoise values from the list may be based on the privacy parameterobtained by Step 822). In some examples, the amount of noise valuesselected by Step 824 may be controlled according to the privacyparameter obtained by Step 822 (for example, according to an amount ofrandomness specified in the privacy parameter obtained by Step 822).

In some embodiments, Step 826 may combine at least one noise value withan actual property of a subgroup of a group of patients to determine anestimated property of the subgroup of the group of patients. Forexample, Step 826 may combine the at least one noise value selected byStep 824 with an actual property of the subgroup of the group ofpatients defined by the subgroup defining input of Step 802 to determinethe estimated property of the subgroup of the group of patients. Forexample, the actual property of the subgroup of the group of patientsmay include a number, and Step 826 may manipulate the number using anoise value (for example, adding the noise value to the number,multiplying the number by the noise value, etc.) to obtained theestimated property of the subgroup of the group of patients. In anotherexample, the actual property of the subgroup of the group of patientsmay include a list of details, and Step 826 may manipulate the list ofdetails using the at least one noise value (for example, dropping atleast part of the items in the list of details based on the at least onenoise value, adding items to the list of details based on the at leastone noise value, modifying items in the list of details based on the atleast one noise value, switching the order of the items in the list ofdetails based on the at least one noise value, etc.) to obtained theestimated property of the subgroup of the group of patients.

FIG. 8C illustrates an example of a method 830 for enabling graphicalillustration based on private medical information. In this example,method 830 may comprise: receiving an indication that an update to astatus of the user occurred (Step 832), for example after forgoingproviding the first information by Step 812; and in response to theupdate to the status of the user, providing the first information (Step834). In some implementations, method 830 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. For example, in some cases Step 832 and/or Step 834 may beexcluded from method 830. In some implementations, one or more stepsillustrated in FIG. 8C may be executed in a different order and/or oneor more groups of steps may be executed simultaneously and/or aplurality of steps may be combined into single step and/or a single stepmay be broken down to a plurality of steps. In some examples, method 830and/or Step 832 may be executed after forgoing providing the firstinformation by Step 812 of method 800.

In some embodiments, Step 832 may comprise receiving an indication thatan update to a status of the user of Step 802 occurred, for exampleafter forgoing providing the first information by Step 812 of method800. For example, Step 832 may receive the indication from a permissionssystem and/or a permissions database (such as permissions 716), from arepository of regulatory statuses of users, from regulators (such asregulators 650), from a repository of user information, from arepository of employment records (for example, employment records of theuser, employment records of medical care providers 630, employmentrecords of insurers 640, employment records of regulators 650,employment records of researchers 660, employment records offacilitators 670, employment records of medical organization 110A,employment records of team 140A, etc.), and so forth. In one example,Step 832 may read the indication from memory (such as memory units 210,shared memory modules 410, and so forth). In another example, Step 832may receive the indication from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In yet another example, Step 832 may receive theindication from a user (for example, through a user interface, through aweb page, using an input device, and so forth).

In some embodiments, Step 834 may comprise providing the firstinformation in response to the update to the status of the user. Forexample, in response to a first update to the status of the user, Step834 may provide the first information, and in response to a secondupdate to the status of the user, Step 834 may forgo providing the firstinformation. Step 834 may provide the first information as describedabove in relation to Step 810.

In some examples, the new status of the user may include a newpermission, Step 832 may receive an indication that an update to thepermissions of the user of Step 802 occurred, and Step 834 may providethe first information in response to the new permission of the user. Forexample, in response to a first new permission of the user, Step 834 mayprovide the first information, and in response to a second newpermission of the user, Step 834 may forgo providing the firstinformation.

In some examples, the new status of the user may include an approval ofan Institutional Review Board for the user to use at least someinformation, Step 832 may receive an indication that an update to theapprovals of the Institutional Review Board for the user of Step 802occurred, and Step 834 may provide the first information in response tothe new approval for the user. For example, in response to a first newapproval for the user, Step 834 may provide the first information, andin response to a second new approval for the user, Step 834 may forgoproviding the first information.

In some examples, the new status of the user may include a newemployment status, Step 832 may receive an indication that an update tothe employment status of the user of Step 802 occurred, and Step 834 mayprovide the first information in response to the new employment statusof the user. For example, in response to a first new employment statusof the user, Step 834 may provide the first information, and in responseto a second new employment status of the user, Step 834 may forgoproviding the first information.

FIG. 8D illustrates an example of a method 840 for enabling graphicalillustration based on private medical information. In this example,method 840 may comprise: receiving an indication that an update to thegroup of patients caused an update to the subgroup defined by thesubgroup defining input (Step 842); determining a size of the updatedsubgroup (Step 844); comparing the determined size of the updatedsubgroup with a selected second subgroup size threshold (Step 846); inresponse to the determined size of the subgroup of the group of patientsbeing smaller than the selected subgroup size threshold and thedetermined size of the updated subgroup being larger than the selectedsecond subgroup size threshold, providing second information (Step 848),the second information may be based on the statistical query and may beconfigured to enable a presentation of a graphical illustration of anestimated property of the updated subgroup; in response to thedetermined size of the subgroup of the group of patients being largerthan the selected subgroup size threshold, forgoing providing the secondinformation (Step 850); and in response to the determined size of theupdated subgroup being smaller than the selected second subgroup sizethreshold, forgoing providing the second information (Step 852). In someimplementations, method 840 may comprise one or more additional steps,while some of the steps listed above may be modified or excluded. Forexample, in some cases Step 842 and/or Step 844 and/or Step 846 and/orStep 848 and/or Step 850 and/or Step 852 may be excluded from method840. In some implementations, one or more steps illustrated in FIG. 8Dmay be executed in a different order and/or one or more groups of stepsmay be executed simultaneously and/or a plurality of steps may becombined into single step and/or a single step may be broken down to aplurality of steps. In some examples, method 840 and/or Step 842 may beexecuted after method 800.

In some embodiments, Step 842 may comprise receiving an indication thatan update to the group of patients caused an update to the subgroupdefined by the subgroup defining input of Step 802. For example, thegroup of patients may be patients 610, and Step 842 may receive anindication that an update to patients 610 caused an update to thesubgroup defined by the subgroup defining input of Step 802. In anotherexample, Step 842 may read the indication from memory (such as memoryunits 210, shared memory modules 410, and so forth). In another example,Step 842 may receive the indication from an external device over acommunication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, and so forth). In yet another example, Step842 may receive the indication from a user (for example, through a userinterface, through a web page, using an input device, and so forth). Inone example, the updated subgroup of Step 842 may include at least onepatient not included in the subgroup of the group of patients defined bythe subgroup defining input of Step 802. In another example, thesubgroup of the group of patients defined by the subgroup defining inputof Step 802 may include at least one patient not included in the updatedsubgroup of Step 842. In some other examples, the size of the updatedsubgroup of Step 842 may be larger than the size of the subgroup of thegroup of patients defined by the subgroup defining input of Step 802,may be identical to the size of the subgroup of the group of patientsdefined by the subgroup defining input of Step 802, may be smaller thanthe size of the subgroup of the group of patients defined by thesubgroup defining input of Step 802, may be different than the size ofthe subgroup of the group of patients defined by the subgroup defininginput of Step 802, and so forth.

In some embodiments, Step 844 may comprise determining a size of theupdated subgroup of Step 842, for example using Step 806 as describedabove.

In some embodiments, Step 846 may comprise comparing the size of theupdated subgroup determined by Step 844 with a selected second subgroupsize threshold. For example, the selected second subgroup size thresholdmay be identical to the selected subgroup size threshold of method 800,may be different from the selected subgroup size threshold of method800, may be larger than the selected subgroup size threshold of method800, may be smaller than the selected subgroup size threshold of method800, and so forth. In some examples, Step 846 may select the secondsubgroup size threshold based on the selected subgroup size threshold ofmethod 800. For example, in response to a first selected subgroup sizethreshold of method 800, Step 846 may select a first value for thesecond subgroup size threshold, and in response to a second selectedsubgroup size threshold of method 800, Step 846 may select a secondvalue for the second subgroup size threshold (the second value differsfrom the first value). In some examples, Step 846 may obtain and/orselect the second subgroup size threshold in a similar fashion to theselection of the selected subgroup size threshold of method 800 by Step808 described above.

In some embodiments, Step 848 may comprise providing second information,for example in response to the size of the subgroup of the group ofpatients of method 800 determined by Step 806 being smaller than theselected subgroup size threshold of Step 808 and the size of the updatedsubgroup determined by Step 844 being larger than the selected secondsubgroup size threshold of Step 846. In some examples, the secondinformation may be based on a statistical query (for example, on thestatistical query of method 800). In some examples, the secondinformation may be configured to enable a presentation of a graphicalillustration of an estimated property of the updated subgroup of Step842, for example to the user of method 800. For example, Step 848 mayprovide the second information in a similar fashion to the providence ofthe first information by Step 810 as described above. In some examples,the second information provided by Step 848 may be identical to thefirst information of method 800, may be different from the firstinformation of method 800, and so forth.

In some embodiments, Step 850 may comprise forgoing and/or withholdingproviding the second information of Step 848, for example in response tothe size of the subgroup of the group of patients of method 800determined by Step 806 being larger than the selected subgroup sizethreshold of Step 808.

In some embodiments, Step 852 may comprise forgoing and/or withholdingproviding the second information of Step 848, for example in response tothe size of the updated subgroup determined by Step 844 being smallerthan the selected second subgroup size threshold of Step 846.

FIG. 8E illustrates an example of a method 860 for enabling graphicalillustration based on private medical information. In this example,method 860 may comprise: receiving an indication that an update to thegroup of patients caused an update to the subgroup defined by thesubgroup defining input (Step 842); determining a size of the updatedsubgroup (Step 844); comparing the determined size of the updatedsubgroup with a selected second subgroup size threshold (Step 846); inresponse to the determined size of the subgroup of the group of patientsbeing larger than the selected subgroup size threshold and thedetermined size of the updated subgroup being larger than the selectedsecond subgroup size threshold, providing second information (Step 868),the second information may be based on the statistical query and may beconfigured to enable a presentation of a graphical illustration of anestimated property of the updated subgroup; and in response to thedetermined size of the updated subgroup being smaller than the selectedsecond subgroup size threshold, forgoing providing the secondinformation (Step 870). In some implementations, method 860 may compriseone or more additional steps, while some of the steps listed above maybe modified or excluded. For example, in some cases Step 842 and/or Step844 and/or Step 846 and/or Step 868 and/or Step 870 may be excluded frommethod 860. In some implementations, one or more steps illustrated inFIG. 8E may be executed in a different order and/or one or more groupsof steps may be executed simultaneously and/or a plurality of steps maybe combined into single step and/or a single step may be broken down toa plurality of steps. In some examples, method 860 and/or Step 862 maybe executed after providing the first information by Step 810 of method800.

In some embodiments, Step 868 may comprise providing second information,for example in response to the determined size of the subgroup of thegroup of patients of method 800 determined by Step 806 being larger thanthe selected subgroup size threshold of Step 808 and the size of theupdated subgroup determined by Step 844 being larger than the selectedsecond subgroup size threshold of Step 846. In some examples, the secondinformation may be based on a statistical query (for example, on thestatistical query of method 800). In some examples, the secondinformation may be configured to enable a presentation of a graphicalillustration of an estimated property of the updated subgroup of Step842, for example to the user of method 800. For example, Step 868 mayprovide the second information in a similar fashion to the providence ofthe first information by Step 810 as described above. In some examples,the second information provided by Step 868 may be identical to thefirst information of method 800, may be different from the firstinformation of method 800, and so forth.

In some embodiments, Step 870 may comprise forgoing and/or withholdingproviding the second information of Step 868, for example in response tothe size of the updated subgroup determined by Step 844 being smallerthan the selected second subgroup size threshold of Step 846.

In some embodiments, method 860 may further comprise forgoing and/orwithholding providing the second information of Step 868, for example inresponse to the size of the subgroup of the group of patients of method800 determined by Step 806 being smaller than the selected subgroup sizethreshold of Step 808.

FIG. 8F illustrates an example of a method 880 for enabling graphicalillustration based on private medical information. In this example,method 880 may comprise: receiving a second subgroup defining input(Step 882), for example after providing the first information, thesecond subgroup defining input may be based on a third input from theuser and may define a second subgroup of the group of patients;receiving a second statistical query about the second subgroup of thegroup of patients (Step 884), the second statistical query may be basedon a fourth input from the user; determining a size of a differencebetween the subgroup of the group of patients and the second subgroup ofthe group of patients (Step 886); in response to the determined size ofthe difference between the subgroup of the group of patients and thesecond subgroup of the group of patients being larger than a selecteddifference threshold, providing second information (Step 888), thesecond information may be based on the second statistical query and maybe configured to enable a presentation of a graphical illustration of anestimated property of the second subgroup of the group of patients, forexample in response to the fourth input from the user; and in responseto the determined size of the difference between the subgroup of thegroup of patients and the second subgroup of the group of patients beingsmaller than a selected difference threshold, forgoing providing thesecond information (Step 890). In some implementations, method 880 maycomprise one or more additional steps, while some of the steps listedabove may be modified or excluded. For example, in some cases Step 882and/or Step 884 and/or Step 886 and/or Step 888 and/or Step 890 may beexcluded from method 880. In some implementations, one or more stepsillustrated in FIG. 8F may be executed in a different order and/or oneor more groups of steps may be executed simultaneously and/or aplurality of steps may be combined into single step and/or a single stepmay be broken down to a plurality of steps. In some examples, method 880and/or Step 882 may be executed after providing the first information byStep 810 of method 800.

In some embodiments, Step 882 may comprise receiving a second subgroupdefining input, for example, after providing the first information (forexample using Step 810 or Step 834), where the second subgroup defininginput may define a second subgroup of the group of patients. In someexamples, the second subgroup defining input may be based on an inputfrom a user, such as a third input from the user of method 800. In someexamples, Step 882 may receive the second subgroup defining input in asimilar fashion to the reception of the subgroup defining input by Step802 described above. In some examples, the structure of the secondsubgroup defining input may be similar or identical to the structure ofthe subgroup defining input of Step 802 described above.

In some embodiments, Step 884 may comprise receiving a secondstatistical query about the second subgroup of the group of patientsdefined by the second subgroup defining input of Step 882. In someexamples, the second statistical query may be based on an input from auser, such as a fourth input from the user of method 800. In someexamples, Step 884 may receive the second statistical query in a similarfashion to the reception of the statistical query by Step 804. In someexamples, the structure of the second statistical query may be similaror identical to the structure of the statistical query of Step 804described above.

In some embodiments, Step 886 may comprise determining a size of adifference between the subgroup of the group of patients defined by thesubgroup defining input of Step 802 and the second subgroup of the groupof patients defined by the second subgroup defining input of Step 882.For example, Step 886 may determine an exact size of the difference, anestimated size of the difference, and so forth. In one example, Step 886may determine at least one of the number (and/or a total weightaccording to an assignment of weight to patients) of patients in thesubgroup of the group of patients defined by the subgroup defining inputof Step 802 that are not in the second subgroup of the group of patientsdefined by the second subgroup defining input of Step 882, the number(and/or a total weight according to an assignment of weight to patients)of patients in the second subgroup of the group of patients defined bythe second subgroup defining input of Step 882 that are not in thesubgroup of the group of patients defined by the subgroup defining inputof Step 802, the number (and/or a total weight according to anassignment of weight to patients) of patients that are in both thesubgroup of the group of patients defined by the subgroup defining inputof Step 802 and the second subgroup of the group of patients defined bythe second subgroup defining input of Step 882. Further, in someexamples, Step 886 may determine the size of the difference as a linearcombination and/or a non-linear combination of one or more of the abovedetermined numbers and/or above determined total weights. In someexamples, any function that measures and/or estimates a differenceand/or a distance between two mathematical sets may be used by Step 886to determine the size of the difference.

In some embodiments, Step 888 may comprise providing second information,for example in response to the size of the difference between thesubgroup of the group of patients and the second subgroup of the groupof patients determined by Step 886 being larger than a selecteddifference threshold. In some examples, the second information may bebased on a statistical query (for example, on the second statisticalquery received by Step 884). In some examples, the second informationmay be configured to enable a presentation of a graphical illustrationof an estimated property of the second subgroup of the group of patientsdefined by the second subgroup defining input of Step 882, for examplein response to the fourth input from the user of Step 884. For example,Step 888 may provide the second information in a similar fashion to theprovidence of the first information by Step 810 as described above. Insome examples, the second information provided by Step 888 may beidentical to the first information of method 800, may be different fromthe first information of method 800, and so forth.

In some embodiments, Step 890 may comprise forgoing and/or withholdingproviding the second information of Step 888, for example in response tothe size of the difference between the subgroup of the group of patientsand the second subgroup of the group of patients determined by Step 886being smaller than a selected difference threshold.

In some examples, the difference threshold of Step 888 and Step 890 maybe selected by method 880. For example, the difference threshold may beread from memory (such as memory units 210, shared memory modules 410,and so forth). In another example, the difference threshold may bereceived from an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth). In yet another example, the difference threshold may be receivedfrom a user (for example, through a user interface, through a web page,using an input device, and so forth). In an additional example, thedifference threshold may be received as a configuration parameter, forexample from a configuration file. In some examples, the differencethreshold may be selected based on the selected subgroup size thresholdof method 800. For example, in response to a first selected subgroupsize threshold of method 800, a first value may be selected for thedifference threshold, and in response to a second selected subgroup sizethreshold of method 800, a second value may be selected for thedifference threshold (the second value differs from the first value). Insome examples, the difference threshold may be selected in a similarfashion to the selection of the selected subgroup size threshold ofmethod 800 by Step 808 described above. For example, the differencethreshold may be randomly selected from a distribution of thresholds. Inanother example, the difference threshold may be selected based on atleast one of whether the subgroup of the group of patients includes afirst patient, whether the second subgroup of the group of patientsincludes a first patient, whether the subgroup of the group of patientsincludes at least a selected number of patients of a first type (whereinthe selected number of patients is one, is two, is between three andfive, is above five, and so forth), whether the second subgroup of thegroup of patients includes at least a selected number of patients of afirst type (wherein the selected number of patients is one, is two, isbetween three and five, is above five, and so forth), the determinedsize of the subgroup of the group of patients, the size of the secondsubgroup of the group of patients, the size of the group of patients, atype of the property of the subgroup of the group of patients, a type ofthe property of the second subgroup of the group of patients, the user,an identity of the user, a type of the user, an identity of a group ofat least two users that includes the user, past behavior of the user, atleast one previous statistical query that is based on an input from theuser, information provided in response to at least one previousstatistical query that is based on an input from the user, and so forth.

In some embodiments, a user (such as user 146) may use a computerizeddata analysis device (such as computerized data analysis device 144) toenter input (such as the first input from the user of Step 802, thesecond input from the user of Step 804, the third input from the user ofStep 882, the fourth input from the user of Step 884, and so forth). Thecomputerized data analysis device may generate a subgroup defining input(such as the subgroup defining input of Step 802, the second subgroupdefining input of Step 882, etc.) and/or a statistical query (such asthe statistical query received by Step 804, the second statistical queryreceived by Step 884, and so forth) based on the input entered by theuser. The computerized data analysis device may provide the generatedsubgroup defining input and/or statistical query to medical organization110 (for example, to a computerized device within medical organization110, to a software of medical organization 110 executed on cloudplatform 400, etc.), for example by transmitting the generated subgroupdefining input and/or statistical query through communication network130. Medical organization 110 (for example, using a computerized device,using a software program, etc.) may use methods 800, 820, 830, 840, 860and 880 to generate a response to the statistical query includinginformation (such as the first information provided by Step 810 or Step834, the second information provided by Step 848, Step 868 or Step 888,and so forth), and the generated response may be provided back tocomputerized data analysis device, for example by transmitting thegenerated response through communication network 130. Further, in someexamples, the computerized data analysis device may receive thegenerated response, and present a graphical illustration of an estimatedproperty of a subgroup of the group of patients (for example, of anestimated property of the subgroup of the group of patients defined bythe subgroup defining input of Step 802, by the second subgroup defininginput of Step 882, etc.), for example in response to the input enteredby the user (for example, in response to the first input from the userof Step 802, to the second input from the user of Step 804, to the thirdinput from the user of Step 882, to the fourth input from the user ofStep 884, and so forth). In some examples, the graphical illustrationmay be further based on information from public data 120 and/orproprietary medical data 142. In some examples, the graphicalillustration may be further based on information received from multiplemedical organizations, such as medical organizations 110A, 110B and110C. For example, each medical organization may provide information asdescribed above, the provided information from the multiple medicalorganizations may be combined, and the graphical illustration may bebased on the combined information.

FIG. 9A illustrates an example of a method 900 for selectively providinginformation about medical data. In this example, method 900 maycomprise: receiving a first statistical query about medical data (Step902), the first statistical query may be based on an input from a firstuser; providing a first estimated property of the medical data to thefirst user (Step 904), the first estimated property of the medical datamay be based on the first statistical query; receiving a secondstatistical query about the medical data (Step 906), the secondstatistical query may be based on an input from a second user; selectinga first group of users that includes the first user (Step 908);determining whether the first group of users includes the second user(Step 910); in response to a determination that the first group of usersdoes not include the second user, providing a second estimated propertyof the medical data to the second user (Step 912), the second estimatedproperty of the medical data may be based on the second statisticalquery; and in response to a determination that the first group of usersincludes the second user, forgoing providing the second estimatedproperty of the medical data to the second user (Step 914). In someimplementations, method 900 may comprise one or more additional steps,while some of the steps listed above may be modified or excluded. Forexample, in some cases Step 902 and/or Step 904 and/or Step 906 and/orStep 908 and/or Step 910 and/or Step 912 and/or Step 914 may be excludedfrom method 900. In some implementations, one or more steps illustratedin FIG. 9A may be executed in a different order and/or one or moregroups of steps may be executed simultaneously and/or a plurality ofsteps may be combined into single step and/or a single step may bebroken down to a plurality of steps. In some examples, after completionof Step 912, method 900 may continue to execute method 940. In someexamples, after completion of Step 912, method 900 may continue toexecute method 960. In some examples, after completion of Step 914,method 900 may continue to execute method 930.

In some embodiments, Step 902 may comprise receiving a first statisticalquery about medical data. In some examples, the first statistical queryreceived by Step 902 may be based on an input from a first user (such asuser 146). In some embodiments, Step 906 may comprise receiving a secondstatistical query about the medical data. In some examples, the secondstatistical query received by Step 906 may be based on an input from asecond user (such as user 146, user 147, user 148, and so forth). Insome examples, the second user may differ from the first user, thesecond user may be the first user, and so forth. In some embodiments,Step 902 and/or Step 906 may read at least part of the statistical queryabout the medical data from memory (such as memory units 210, sharedmemory modules 410, and so forth). In another example, Step 902 and/orStep 906 may receive at least part of the statistical query about themedical data from an external device (such as computerized data analysisdevice 144, an external device associated with the user, such as aworkstation, a mobile device of the user, etc.) over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,etc.), may receive at least part of the statistical query about themedical data from the user (for example, through a user interface,through a web page, using an input device, through computerized dataanalysis device 144, etc.), and so forth. In one example, thestatistical query about the medical data received by Step 902 and/or thestatistical query about the medical data received by Step 906 mayinclude a query in a query language (such as Structured Query Language)that expresses a statistical query about the medical data. In anotherexample, the statistical query about the medical data received by Step902 and/or the statistical query about the medical data received by Step906 may include one or more mathematical formulas for calculating astatistical measure of the medical data.

In some embodiments, Step 904 may comprise providing a first estimatedproperty of the medical data, for example to the first user of Step 902.In some examples, the first estimated property of the medical data maybe based on a statistical query (for example, on the first statisticalquery received by Step 902). For example, Step 904 may store the firstestimated property of the medical data in memory (such as memory 700,memory units 210, memory modules 410, etc.), in storage device (such aslocal storage, remote storage, network attached storage, etc.), and soforth. In another example, Step 904 may transmit the first estimatedproperty of the medical data to an external device, for example over acommunication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, and so forth). In some examples, Step 904 maypresent the first estimated property of the medical data to a user (suchas the first user of Step 902), for example through a user interface,through a web page, using an output device (such as a display screen, anaugmented reality display system, a printer, a LED indicator, etc.),through computerized data analysis device 144, and so forth. In oneexample, the first estimated property of the medical data provided byStep 904 may be an actual property of the medical data. In anotherexample, the first estimated property of the medical data provided byStep 904 may be an approximation of an actual property of the medicaldata.

In some embodiments, Step 908 may comprise selecting a first group ofusers that includes the first user of Step 902. For example, the firstgroup of users may include any number of users (such as one user, twousers, between three and ten users, more than ten users, and so forth).In some examples, Step 908 may select the first group of users of aplurality of alternative groups of users. For example, Step 908 mayselect the first group of users of a plurality of alternative groups ofusers based on an identity of the first user of Step 902. In anotherexample, a group of users that maximize a particular criterion function(such as, largest group, smallest group, etc.) from all the groups ofusers in the plurality of alternative groups of users that includes thefirst user may be selected. In some examples, the first group of usersmay be generated, for example by selecting users from a plurality ofusers according to a user selection rule. In some examples, the firstgroup of users may be selected based on user input. For example, theuser may specify the users in the first group of users, may select thefirst group of users from a plurality of alternative groups, may specifya user selection rule, and so forth. In some examples, the first groupof users may be read from memory (such as memory units 210, sharedmemory modules 410, and so forth). In some examples, a type of the firstuser of Step 902 may be a particular type, and Step 908 may select thefirst group of users to include all users of the particular type from aparticular plurality of users.

In some embodiments, Step 910 may comprise determining whether the firstgroup of users selected by Step 908 includes the second user of Step906. For example, a list of the users in the group of users may beaccessed to determine whether the second user of Step 906 is in thelist, and therefore determine whether the first group of users selectedby Step 908 includes the second user of Step 906. In another example, auser selection rule defining which users are in the group of users maybe used to determine whether the first group of users selected by Step908 includes the second user of Step 906.

In some embodiments, Step 912 may comprise providing a second estimatedproperty of the medical data, for example to the second user of Step906, for example in response to a determination by Step 910 that thefirst group of users selected by Step 908 does not include the seconduser of Step 906. In some examples, the second estimated property of themedical data provided by Step 912 and/or Step 934 and/or Step 952 may bebased on a statistical query (for example, on the second statisticalquery received by Step 906). For example, Step 912 and/or Step 934and/or Step 952 may provide the second estimated property of the medicaldata in a similar fashion to the providence of the first estimatedproperty of the medical data by Step 904 described above. In oneexample, the second estimated property of the medical data provided byStep 912 and/or by Step 934 and/or by Step 952 may be an actual propertyof the medical data. In another example, the second estimated propertyof the medical data provided by Step 912 and/or by Step 934 and/or byStep 952 may be an approximation of an actual property of the medicaldata.

In some embodiments, Step 914 may comprise forgoing and/or withholdingproviding the second estimated property of the medical data of Step 912to the second user of Step 906, for example in response to adetermination by Step 910 that the first group of users selected by Step908 includes the second user of Step 906.

Additionally or alternatively, in response to a determination that thefirst group of users includes the second user, Step 914 may cause asuggestion of an alternative statistical query to be provided to thesecond user of Step 906. In some examples, the alternative statisticalquery may be based on the second statistical query received by Step 906.For example, Step 914 may select the alternative statistical query froma plurality of possible statistical queries based on the secondstatistical query received by Step 906. In another example, Step 914 mayuse a function to transform the second statistical query received byStep 906 into the alternative statistical query. In some examples, Step914 may provide a suggestion of the alternative statistical query to thesecond user, may cause an external device (such as computerized dataanalysis device 144) to provide the alternative statistical query to thesecond user (for example, by transmitting information configured tocause the external device to provide the alternative statistical queryto the second user), and so forth. Further, in some examples, Step 914may receive an indication that the second user of Step 906 accepted thesuggested alternative statistical query. For example, Step 914 mayreceive user input from the second user indicating that the second useraccepted the suggested alternative statistical query, may receiveinformation from an external device (such as computerized data analysisdevice 144) indicating that the second user accepted the suggestedalternative statistical query, and so forth. Further, in some examples,in response to the received indication that the second user accepted thesuggested alternative statistical query, Step 914 may provide a thirdestimated property of the medical data to the second user. For example,the third estimated property of the medical data may be based on thesuggested alternative statistical query. For example, Step 914 mayprovide the third estimated property of the medical data in a similarfashion to the providence of the first estimated property of the medicaldata by Step 904 described above.

In some embodiments, Step 904 may further comprise updating a privacybudget associated with the first group of users of Step 908 to reflectthe providence of the first estimated property of the medical data tothe first user of Step 902 by Step 904. For example, a first privacyconsumption may be determined based on the first statistical query ofStep 902 and/or based on the first estimated property of the medicaldata, and the privacy budget associated with the first group of users ofStep 908 may be updated based on the determined first privacyconsumption (for example, by reducing the first privacy consumption fromthe privacy budget, by multiplying the privacy budget by a factorselected based on the first privacy consumption, and so forth). Forexample, a machine learning model may be trained using training examplesto determine privacy consumption values from queries and/or providedinformation, and the trained machine learning model may be used toanalyze the first statistical query of Step 902 and/or the firstestimated property of the medical data to determine the first privacyconsumption. An example of such training example may include a record ofa query and/or of provided information, together with a label indicatingthe desired privacy consumption value to be determined.

Further, in some embodiments, Step 910 may further determine whether theupdated privacy budget is sufficient for the second statistical query.For example, a projected privacy consumption may be determined based onthe first statistical query of Step 906 and/or based on the secondestimated property of the medical data to be provided by Step 912 (forexample in a similar fashion to the determination of the first privacyconsumption described above), and the updated privacy budget may becompared with the determined projected privacy consumption to determinewhether the updated privacy budget is sufficient for the secondstatistical query.

Further, in some embodiments, in response to the determination by Step910 that the first group of users selected by Step 908 includes thesecond user of Step 906 and a determination by Step 910 that the updatedprivacy budget is sufficient for the second statistical query of Step906, the second estimated property of the medical data may be providedto the second user (for example as described above in relation to Step912), and in response to the determination by Step 910 that the firstgroup of users selected by Step 908 includes the second user of Step 906and a determination by Step 910 that the updated privacy budget isinsufficient for the second statistical query of Step 906, providing thesecond estimated property of the medical data to the second user may bewithheld and/or forwent.

Further, in some embodiments, after Step 910 determines whether theupdated privacy budget is sufficient for the second statistical query, athird statistical query about the medical data may be received. Forexample, the third statistical query may be based on an additional inputfrom the first user. For example, the third statistical query about themedical data may be received using Step 962. Further, in some examples,in case the first group of users selected by Step 908 includes thesecond user of Step 906 and the second estimated property of the medicaldata was not provided to the second user, a third estimated property ofthe medical data may be provided to the first user (for example, thethird estimated property of the medical data may be based on the thirdstatistical query), and in case the first group of users selected byStep 908 includes the second user of Step 906 and the second estimatedproperty of the medical data was provided to the second user, providingthe third estimated property of the medical data to the first user maybe withheld and/or forwent. For example, the third estimated property ofthe medical data may be provided in a similar fashion to the providenceof the first estimated property of the medical data by Step 904described above.

FIG. 9B illustrates an example of a method 920 for determining anestimated property of medical data. In this example, method 920 maycomprise: obtaining a privacy parameter (Step 822); selecting at leastone noise value based on the obtained privacy parameter (Step 824); andcombining the at least one noise value with an actual property of themedical data to determine an estimated property of the medical data(Step 926). In some implementations, method 920 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. For example, in some cases Step 822 and/or Step 824 and/orStep 926 may be excluded from method 920. In some implementations, oneor more steps illustrated in FIG. 9B may be executed in a differentorder and/or one or more groups of steps may be executed simultaneouslyand/or a plurality of steps may be combined into single step and/or asingle step may be broken down to a plurality of steps.

In some embodiments, Step 926 may comprise combining the at least onenoise value selected by Step 824 with an actual property of the medicaldata to determine an estimated property of the medical data. Forexample, the actual property of the medical data may include a number,and Step 926 may manipulate the number using a noise value (for example,adding the noise value to the number, multiplying the number by thenoise value, etc.) to obtained the estimated property of the medicaldata. In another example, the actual property of the medical data mayinclude a list of details, and Step 926 may manipulate the list ofdetails using the at least one noise value (for example, dropping atleast part of the items in the list of details based on the at least onenoise value, adding items to the list of details based on the at leastone noise value, modifying items in the list of details based on the atleast one noise value, switching the order of the items in the list ofdetails based on the at least one noise value, etc.) to obtained theestimated property of the medical data.

In some embodiments, method 920 may be used one or more times. Forexample, method 920 may be used at a first time to determine the firstestimated property of the medical data of Step 904, may be used at asecond time to determine the second estimated property of the medicaldata of Step 912, and so forth. In some embodiments, Step 822 may obtaina first privacy parameter, Step 824 may select a first at least onenoise value based on the first privacy parameter obtained by Step 822,and Step 926 may combine the first at least one noise value selected byStep 824 with a first actual property of the medical data to determinethe first estimated property of the medical data of Step 904. Further,in some examples, Step 822 may obtain a second privacy parameter, Step824 may select a second at least one noise value based on the secondprivacy parameter obtained by Step 822, and Step 926 may combine thesecond at least one noise value selected by Step 824 with a secondactual property of the medical data to determine the second estimatedproperty of the medical data of Step 912. In one example, the firstprivacy parameter and the second privacy parameter may be identical. Inanother example, the first privacy parameter may differ from the secondprivacy parameter. In one example, the first actual property of themedical data may be identical to the second actual property of themedical data. In another example, the first actual property of themedical data may differ from the second actual property of the medicaldata. In some examples, in response to the determination by Step 910that the first group of users selected by Step 908 includes the seconduser of Step 906 and a first value of the first privacy parameterobtained by Step 822, the second estimated property of the medical datamay be provided to the second user (for example in a similar fashion tothe providence of the first estimated property of the medical data byStep 904 described above), and in response to the determination by Step910 that the first group of users selected by Step 908 includes thesecond user of Step 906 and a second value of the obtained first privacyparameter, providing the second estimated property of the medical datato the second user may be withheld and/or forwent.

FIG. 9C illustrates an example of a method 930 for selectively providinginformation about medical data. In this example, method 930 maycomprise: receiving an indication that a particular change occurred tothe first group of users (Step 932), for example after forgoingproviding the second estimated property of the medical data to thesecond user by Step 914; and in response to the received indication,providing the second estimated property of the medical data to thesecond user (Step 934). In some implementations, method 930 may compriseone or more additional steps, while some of the steps listed above maybe modified or excluded. For example, in some cases Step 932 and/or Step934 may be excluded from method 930. In some implementations, one ormore steps illustrated in FIG. 9C may be executed in a different orderand/or one or more groups of steps may be executed simultaneously and/ora plurality of steps may be combined into single step and/or a singlestep may be broken down to a plurality of steps. In some examples,method 930 and/or Step 932 may be executed after forgoing providing thefirst information by Step 914 of method 900. In some examples, aftercompletion of Step 934, method 930 may continue to execute method 940.In some examples, after completion of Step 934, method 930 may continueto execute method 960.

In some embodiments, Step 932 may comprise receiving, after Step 910determines that the first group of users includes the second user and/orafter Step 914 forgoes providing the second estimated property of themedical data to the second user, an indication that a particular changeoccurred to the first group of users, for example an indication that thefirst group of users changed to exclude a particular user, such as thefirst user of Step 902, the second user of Step 906, a different user,and so forth. For example, Step 932 may receive the indication from apermissions system and/or a permissions database (such as permissions716), from a repository of regulatory statuses of users, from regulators(such as regulators 650), from a repository of user information, from arepository of employment records (for example, employment records of theuser, employment records of medical care providers 630, employmentrecords of insurers 640, employment records of regulators 650,employment records of researchers 660, employment records offacilitators 670, employment records of medical organization 110A,employment records of team 140A, etc.), and so forth. In one example,Step 932 may read the indication from memory (such as memory units 210,shared memory modules 410, and so forth). In another example, Step 932may receive the indication from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In yet another example, Step 932 may receive theindication from a user (for example, through a user interface, through aweb page, using an input device, and so forth).

In some embodiments, Step 934 may comprise providing the secondestimated property of the medical data to the second user in response toan indication received by Step 932. For example, Step 934 may providethe second estimated property of the medical data to the second user inresponse to a first indication received by Step 932, and may withholdand/or forgo providing second estimated property of the medical data tothe second user in response to a second indication received by Step 932.In another example, Step 934 may provide the second estimated propertyof the medical data to the second user in response to an indicationreceived by Step 932 that the first group of users changed to excludeone user, and may withhold and/or forgo providing second estimatedproperty of the medical data to the second user in response to anindication received by Step 932 that the first group of users changed toexclude a different user. In one example, Step 934 may compriseproviding the second estimated property of the medical data to thesecond user in response to an indication that the first group of userschanged to exclude the second user received by Step 932. In anotherexample, Step 934 may comprise providing the second estimated propertyof the medical data to the second user in response to an indication thatthe first group of users changed to exclude the first user received byStep 932.

FIG. 9D illustrates an example of a method 940 for selectively providinginformation about medical data. In this example, method 940 maycomprise: receiving an indication that the first group of users changedto include the second user (Step 942), for example after providing thesecond estimated property of the medical data to the second user by Step912 or by Step 934 or by Step 952; and in response to the receivedindication, providing a notification (Step 944). In someimplementations, method 940 may comprise one or more additional steps,while some of the steps listed above may be modified or excluded. Forexample, in some cases Step 942 and/or Step 944 may be excluded frommethod 940. In some implementations, one or more steps illustrated inFIG. 9D may be executed in a different order and/or one or more groupsof steps may be executed simultaneously and/or a plurality of steps maybe combined into single step and/or a single step may be broken down toa plurality of steps. In some examples, method 940 and/or Step 942 maybe executed after providing the first information by Step 912 of method900.

In some embodiments, Step 942 may comprise receiving, after Step 910determines that the first group of users does not include the seconduser and/or after Step 912 or Step 934 or Step 952 provide the secondestimated property of the medical data to the second user, an indicationthat the first group of users changed to include the second user. Forexample, Step 942 may receive the indication from a permissions systemand/or a permissions database (such as permissions 716), from arepository of regulatory statuses of users, from regulators (such asregulators 650), from a repository of user information, from arepository of employment records (for example, employment records of theuser, employment records of medical care providers 630, employmentrecords of insurers 640, employment records of regulators 650,employment records of researchers 660, employment records offacilitators 670, employment records of medical organization 110A,employment records of team 140A, etc.), and so forth. In one example,Step 942 may read the indication from memory (such as memory units 210,shared memory modules 410, and so forth). In another example, Step 942may receive the indication from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In yet another example, Step 942 may receive theindication from a user (for example, through a user interface, through aweb page, using an input device, and so forth).

In some embodiments, Step 944 may comprise providing a notification inresponse to the providence of the first estimated property of themedical data to the first user by Step 904, and/or to the providence ofthe second estimated property of the medical data to the second user byStep 912 or Step 934 or Step 952, and/or to the indication received byStep 942 that the first group of users changed to include the seconduser. In some examples, Step 944 may provide the notification to a user,to a log file, to another process, to an external device, and so forth.For example, Step 944 may store the notification in memory (such asmemory 700, memory units 210, memory modules 410, etc.), in storagedevice (such as local storage, remote storage, network attached storage,etc.), and so forth. In another example, Step 944 may transmit thenotification to an external device, for example over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In some examples, Step 944 may present the notificationto a user, for example through a user interface, through a web page,using an output device (such as a display screen, an augmented realitydisplay system, a printer, a LED indicator, etc.), through computerizeddata analysis device 144, and so forth.

FIG. 9E illustrates an example of a method 950 for selectively providinginformation about medical data. In this example, method 950 maycomprise: in response to the second statistical query of Step 906belonging to a selected group of statistical queries and thedetermination by Step 910 that the first group of users of Step 908includes the second user of Step 906, providing the second estimatedproperty of the medical data to the second user (Step 952); and inresponse to the second statistical query of Step 906 not belonging tothe selected group of statistical queries and the determination by Step910 that the first group of users of Step 908 includes the second userof Step 906, forgoing providing the second estimated property of themedical data to the second user (Step 954). In some implementations,method 950 may comprise one or more additional steps, while some of thesteps listed above may be modified or excluded. For example, in somecases Step 952 and/or Step 954 may be excluded from method 950. In someimplementations, one or more steps illustrated in FIG. 9E may beexecuted in a different order and/or one or more groups of steps may beexecuted simultaneously and/or a plurality of steps may be combined intosingle step and/or a single step may be broken down to a plurality ofsteps. In some examples, method 950 may be executed after Step 910 ofmethod 900, for example instead of Steps 914 of method 900. In someexamples, after completion of Step 952, method 950 may continue toexecute method 940. In some examples, after completion of Step 952,method 950 may continue to execute method 960.

In one example, the selected group of statistical queries of method 950may include a query about a total number of patients in the medicaldata, may exclude a query about a number of patients that match aparticular criterion, and so forth. In some examples, the selected groupof statistical queries of method 950 may include a query about astatistic of a particular property in the medical data of patients in afirst group of patients when the size of the first group of patients isabove a selected threshold, and may exclude the query about thestatistic of the particular property in the medical data of patients inthe first group of patients when the size of the first group of patientsis below the selected threshold. For example, the selected threshold maybe based on the particular property. In another example, the selectedthreshold may be based on the second user of Step 906.

In some embodiments, Step 952 may comprise providing the secondestimated property of the medical data of Step 912, for example to thesecond user of Step 906, for example in response to the secondstatistical query of Step 906 belonging to a selected group ofstatistical queries and/or to the determination by Step 910 that thefirst group of users of Step 908 includes the second user of Step 906.

In some embodiments, Step 954 may comprise forgoing providing the secondestimated property of the medical data of Step 912 to the second user ofStep 906 in response to the second statistical query of Step 906 notbelonging to the selected group of statistical queries and/or thedetermination by Step 910 that the first group of users of Step 908includes the second user of Step 906.

FIG. 9F illustrates an example of a method 960 for selectively providinginformation about medical data. In some examples, method 960 as well asall individual steps therein may be performed after Step 910 determinedthat the first group of users does not include the second user and/orafter Step 912 or by Step 934 or by Step 952 provided the secondestimated property of the medical data to the second user. In thisexample, method 960 may comprise: receiving a third statistical queryabout the medical data (Step 962), the third statistical query may bebased on an additional input from the first user of Step 902;determining whether the first group of users selected by Step 908changed to include the second user of Step 906 (Step 964); in responseto a determination that the first group of users did not change toinclude the second user, providing a third estimated property of themedical data to the first user of Step 902 (Step 966), the thirdestimated property of the medical data may be based on the thirdstatistical query; and in response to a determination that the firstgroup of users changed to include the second user, forgoing providingthe third estimated property of the medical data to the first user (Step968). In some implementations, method 960 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. For example, in some cases Step 962 and/or Step 964 and/orStep 966 and/or Step 968 may be excluded from method 960. In someimplementations, one or more steps illustrated in FIG. 9F may beexecuted in a different order and/or one or more groups of steps may beexecuted simultaneously and/or a plurality of steps may be combined intosingle step and/or a single step may be broken down to a plurality ofsteps.

In some embodiments, Step 962 may comprise receiving a third statisticalquery about the medical data, for example after Step 910 determined thatthe first group of users does not include the second user and/or afterStep 912 or by Step 934 or by Step 952 provided the second estimatedproperty of the medical data to the second user. For example, the thirdstatistical query is based on an additional input from the first user.For example, Step 962 may receive the third statistical query about themedical data in a similar fashion to the reception of the firststatistical query about the medical data by Step 902 described above.

In some embodiments, Step 964 may comprise determining whether the firstgroup of users selected by Step 908 changed to include the second userof Step 906, for example after Step 910 determined that the first groupof users does not include the second user and/or after Step 912 or byStep 934 or by Step 952 provided the second estimated property of themedical data to the second user. For an indication that the first groupof users selected by Step 908 changed to include the second user of Step906 may be received by Step 942 as described above, in response to suchreceived indication, Step 964 may determine that the first group ofusers selected by Step 908 changed to include the second user of Step906, and in response to a lack of such indication, Step 964 maydetermine that the first group of users selected by Step 908 did notchange to include the second user of Step 906.

In some embodiments, Step 966 may comprise providing a third estimatedproperty of the medical data to the first user of Step 902, for examplein response to a determination by Step 964 that the first group of usersselected by Step 908 did not change to include the second user of Step906. For example, the third estimated property of the medical data maybe based on the third statistical query. In some examples, Step 966 mayprovide the third estimated property of the medical data in a similarfashion to the providence of the first estimated property of the medicaldata by Step 904 described above.

In some embodiments, Step 968 may comprise forgoing providing the thirdestimated property of the medical data of Step 966 to the first user ofStep 902, for example in response to a determination by Step 964 thatthe first group of users selected by Step 908 changed to include thesecond user of Step 906.

FIG. 10 illustrates an example of a method 1000 for controlling accessto private information, for example to private medical information. Forexample, method 1000 may control access to private information (such asprivate medical information) in privacy firewalls. In the example ofFIG. 10, method 1000 may comprise: receiving a request to access acontent of an element, the content of the element includes at least afirst portion and a second portion, the first portion includesidentifiable information and the second portion does not includeidentifiable information (Step 1002), accessing a permission recordcorresponding to the element (Step 1004), in response to a first valuein the permission record, providing access to the content of theelement, including access to the first portion and the second portion ofthe content of the element (Step 1006), and in response to a secondvalue in the permission record, providing partial access to the contentof the element, the partial access includes access to the second portionof the content of the element and excludes access to the first portionof the content of the element (Step 1008). In some implementations,method 1000 may comprise one or more additional steps, while some of thesteps listed above may be modified or excluded. In some implementations,one or more steps illustrated in FIG. 10 may be executed in a differentorder and/or one or more groups of steps may be executed simultaneouslyand/or a plurality of steps may be combined into single step and/or asingle step may be broken down to a plurality of steps.

In some examples, Step 1002 may comprise receiving a request to access acontent of an element, the content of the element may include at least afirst portion and a second portion, the first portion may includeidentifiable information and the second portion may include noidentifiable information. For example, the first portion may includeportion 742 or a portion of data element 750 as described above, and thesecond element may include portion 744 or a portion of data element 750as described above. In one example, Step 1002 may read the request froma memory (such as memory units 210, shared memory modules 410, and soforth). In another example, Step 1002 may receive the request from anexternal device over a communication network using a communicationdevice (such as communication modules 230, internal communicationmodules 440, external communication modules 450, and so forth). In yetanother example, Step 1002 may receive the request from a user (forexample, through a user interface, through a web page, using an inputdevice, and so forth).

In some examples, receiving the request to access the content of theelement by Step 1002 may comprise accessing a stream of digitalcommunication data sent by a first computing device to a secondcomputing device, and analyzing the stream to detect the request in thestream, for example using a pattern matching algorithm, by parsing aprotocol used for communication over the stream, and so forth. In oneexample, the analysis of the stream may be performed by a thirdcomputing device, the third computing device may differ from the firstcomputing device and from the second computing device. In anotherexample, the analysis of the stream may be performed by the secondcomputing device. In yet another example, the analysis of the stream maybe performed by the first computing device. In one example, the streamof digital communication data may be accessed by a privacy firewall, forexample as described above. In another example, the stream of digitalcommunication data may be read from a memory (such as memory units 210,shared memory modules 410, and so forth), may be received from anexternal device over a communication network using a communicationdevice (such as communication modules 230, internal communicationmodules 440, external communication modules 450, and so forth), may bereceived from a communication network using network sniffing techniques,and so forth.

In some examples, Step 1004 may comprise accessing a permission recordcorresponding to an element, for example corresponding to the element ofStep 1002. For example Step 1004 may access the permission record in amemory (such as memory units 210, shared memory modules 410, and soforth). In another example, Step 1004 may access the permission recordat an external device over a communication network using a communicationdevice (such as communication modules 230, internal communicationmodules 440, external communication modules 450, and so forth). In yetanother example, Step 1004 may access the permission record inpermissions 716.

In some examples, Step 1006 may comprise, for example in response to afirst value in the permission record accessed by Step 1004, providingaccess to the content of the element of Step 1002, for example includingaccess to the first portion and the second portion of the content of theelement. In some examples, Step 1008 may comprise, for example inresponse to a second value in the permission record accessed by Step1006, providing partial access to the content of the element of Step1002, the partial access may include access to the second portion of thecontent of the element and may exclude access to the first portion ofthe content of the element.

In some examples, method 1000 may further comprise, for example inresponse to a third value in the permission record accessed by Step1006, providing access to synthetic information. In one example, thesynthetic information may be based on the content of the element of Step1002. In some examples, the synthetic information may be a result ofanalyzing the content of the element of Step 1002 to generate thesynthetic information. For example, the content of the element of Step1002 may be analyzed to determine a distribution of values in particularportions of the element, and the determined distribution of values maybe used to generate the synthetic information. In another example, thecontent of the element of Step 1002 may be used to train a GenerativeAdversarial Network (GAN), and the trained GAN may be used to generatethe synthetic information.

In some examples, method 1000 may further comprise, for example inresponse to a third value in the permission record accessed by Step1006, providing statistical information based on the content of theelement of Step 1002. In some examples, the statistical information maybe a result of analyzing the content of the element of Step 1002 togenerate the statistical information. For example, the content of theelement of Step 1002 may be analyzed to determine a distribution ofvalues in the element, and information may be provided based on thedetermined distribution of values.

In some examples, method 1000 may further comprise, for example inresponse to a third value in the permission record accessed by Step1006, providing statistical information based on a selected portion ofthe content of the element of Step 1002. In some examples, thestatistical information may be a result of analyzing the content of theselected portion of the content of the element Step 1002 to generate thestatistical information. For example, the content of the element of Step1002 may be analyzed to determine a distribution of values in particularportions of the element, and information may be provided based on thedetermined distribution of values. In one example, the selected portionof the content of the element Step 1002 may be selected based on aninput from a user. In another example, the selected portion of thecontent of the element Step 1002 may be selected based on the content ofthe element Step 1002, for example based on an analysis of the contentof the element Step 1002 using an attention model trained to selectportions of elements using training examples. An example of suchtraining example may include a sample element, together with a labelindicating a portion of the sample element to be selected.

In some examples, method 1000 may further comprise, for example inresponse to a third value in the permission record accessed by Step1006, denying access to the content of the element of Step 1002.

In some examples, it may be determined whether a third portion of thecontent of the element of Step 1002 includes identifiable information,for example as described below. In one example, in response to a firstvalue (for example, the first value of Step 1006) in the permissionrecord accessed by Step 1004, method 1000 may provide access to thethird portion of the content of the element of Step 1002, in response toa second value in the permission record accessed by Step 1004 (forexample, the second value of Step 1008) and a determination that thethird portion of the content of the element of Step 1002 includesidentifiable information, method 1000 may deny access to the thirdportion of the content of the element of Step 1002, and in response to asecond value in the permission record accessed by Step 1004 (forexample, the second value of Step 1008) and a determination that thethird portion of the content of the element of Step 1002 do not includeidentifiable information, method 1000 may provide access to the thirdportion of the content of the element of Step 1002.

In some examples, it may be determined whether a portion of the contentof the element includes identifiable information. For example, a machinelearning model may be trained using training examples to determinewhether portions of elements include identifiable information, and thetrained machine learning model may be used to analyze the content of theelement and determined whether the portion of the content of the elementincludes identifiable information. An example of such training examplemay include a sample content and an indication of a portion of thesample content, together with a label indicating whether the indicatedportion of the sample content includes identifiable information. Inanother example, an indication of whether the portion of the content ofthe element includes identifiable information may be obtained (forexample, from a memory, from an external device, from a user, and soforth), and the determination of whether the portion of the content ofthe element includes identifiable information may be based on theobtained indication. In yet another example, the determination ofwhether the portion of the content of the element includes identifiableinformation may be based on type of fields included in the portion ofthe content of the element, may be based on a distribution of values inthe portion of the content of the element, may be based on values in theportion of the content of the element, may be based on an analysis ofvalues in the portion of the content of the element, and so forth.

In some examples, the request to access the content of the elementreceived by Step 1002 may include an indication of a requesting entity,and the Step 1004 may further comprise selecting the permission recordcorresponding to the element of Step 1002 of a plurality of alternativepermission records corresponding to the element of Step 1002 based onthe requesting entity. For example, in response to a first requestingentity, Step 1004 may select a first permission record corresponding tothe element of Step 1002, and in response to a second requesting entity,Step 1004 may select a second permission record corresponding to theelement of Step 1002, the second permission record may differ from thefirst permission record. Some non-limiting examples of such indicationof a requesting entity may include an identifier of the requestingentity (such as a name, an identification number or code, etc.), anindication of a type of the requesting entity, an identification of anorganization affiliated with the requesting entity, an indication of adevice used by the requesting entity for example to generate and/or tosend the request (such as device name, IP address, mac address, etc.),and so forth. Some non-limiting examples of such requesting entity mayinclude may include a person, a researcher, a physician, anorganization, a division within an organization, and so forth.

In some examples, the request to access the content of the elementreceived by Step 1002 may include an indication of an intendent usage,and Step 1004 may further comprise selecting the permission recordcorresponding to the element of Step 1002 of a plurality of alternativepermission records corresponding to the element of Step 1002 based onthe intendent usage. For example, in response to a first intendentusage, Step 1004 may select a first permission record corresponding tothe element of Step 1002, and in response to a second intendent usage,Step 1004 may select a second permission record corresponding to theelement of Step 1002, the second permission record may differ from thefirst permission record. Some non-limiting examples of such intendentusage may include retrieving at least part of the content of theelement, modifying at least part of the content of the element, deletingat least part of the content of the element, calculating a value of afunction using at least part of the content of the element, calculatinga value of a gradient of a function using at least part of the contentof the element, training a machine learning model using at least part ofthe content of the element, generating an artificial neural networkusing at least part of the content of the element, generating aninference model using at least part of the content of the element,generating statistical information using at least part of the content ofthe element, generating synthetic data using at least part of thecontent of the element, and so forth. In one example, in response to anintendent usage of training a first machine learning model using atleast part of the content of the element, Step 1004 may select a firstpermission record corresponding to the element of Step 1002, and inresponse to an intendent usage of training a second learning model usingat least part of the content of the element, Step 1004 may select asecond permission record corresponding to the element of Step 1002, thesecond permission record may differ from the first permission record. Inone example, in response to an intendent usage of calculating a value ofa first function using at least part of the content of the element, Step1004 may select a first permission record corresponding to the elementof Step 1002, and in response to an intendent usage of calculating avalue of a second function using at least part of the content of theelement, Step 1004 may select a second permission record correspondingto the element of Step 1002, the second permission record may differfrom the first permission record.

In some examples, the request to access the content of the elementreceived by Step 1002 may include and/or be a request to access thecontent of the element for a mathematical optimization of a function. Inone example, Step 1008 may further comprise, in response to the secondvalue in the permission record, providing access to an updateinformation for the mathematical optimization of the function calculatedusing the first portion of the content of the element. For example, Step1008 may calculate the update information for the mathematicaloptimization of the function using the first portion of the content ofthe element, and may provide the calculated update information. Somenon-limiting examples of such update information may include step size,step direction, gradient, a selection of a parameter of the function tobe modified, a new value for a parameter of the function, a selection ofa new value of a hyper-parameter of the mathematical optimization, andso forth.

In some examples, the request to access the content of the elementreceived by Step 1002 may include and/or be a request to access thecontent of the element for a mathematical optimization of a function. Inone example, Step 1008 may further comprise, in response to the secondvalue in the permission record, providing access to a value of amathematical expression of a gradient of the function calculated usingthe first portion of the content of the element. For example, Step 1008may calculate the value of the mathematical expression of the gradientof the function using the first portion of the content of the element,and may provide the calculated value of the mathematical expression ofthe gradient of the function.

In some examples, the request to access the content of the elementreceived by Step 1002 may include and/or be a request to access thecontent of the element to determine a value of a function using thefirst portion of the content of the element. In one example, Step 1008may further comprise, in response to the second value in the permissionrecord, providing access to a value of the function calculated using thefirst portion of the content of the element. For example, Step 1008 maycalculate a value of the function using the first portion of the contentof the element, and may provide the calculated value.

In some examples, an input defining a function may be received. Forexample, the input defining the function may be read from memory (suchas memory units 210, shared memory modules 410, and so forth), may bereceived from external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth), may be generated, may be received from a user, and so forth.Some non-limiting examples of such function may include mathematicalfunction, computer function, linear function, non-linear function,polynomial function, continuous function, discontinuous function,differentiable function, non-differentiable function, and so forth. Inone example, Step 1008 may further comprise, in response to the secondvalue in the permission record, causing a usage of an identified copy ofthe element to calculate a value of the function, and causing presentinga de-identified copy of the element with the calculated value of thefunction. In one example, the identified copy of the element and/or thede-identified copy of the element may be obtained as described above.

In some examples, receiving the request to access the content of theelement by Step 1002 may comprise accessing a stream of digitalcommunication data sent by a first computing device to a secondcomputing device (for example as described above), and excluding accessto the first portion of the content of the element by Step 1008 maycomprise accessing a second stream of digital communication data (thesecond stream of digital communication may be a stream sent by thesecond computing device to the first computing device), analyzing thesecond stream to detect information based on at least part of the firstportion of the content of the element in the second stream, and inresponse to a detection of the information based on the at least part ofthe first portion of the content of the element, blocking at least partof the second stream from reaching the first computing device. In oneexample, the second stream of digital communication data may be accessedby a privacy firewall, for example as described above. In anotherexample, the second stream of digital communication data may be readfrom a memory (such as memory units 210, shared memory modules 410, andso forth), may be received from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth), may be received from a communication network usingnetwork sniffing techniques, and so forth. In some examples, analyzingthe second stream to detect information based on at least part of thefirst portion of the content of the element in the second stream may beperformed using a pattern matching algorithm, by parsing a protocol usedfor communication over the stream, by using a machine learning modeltrained using training examples to detect information in data streamsthat is based on particular portions of data elements, and so forth. Insome examples, in response to a detection of the information based onthe at least part of the first portion of the content of the element ina particular part of the second stream, the particular part of thesecond stream may be blocked from reaching the first computing device,and in response to no detection of the information based on the at leastpart of the first portion of the content of the element in theparticular part of the second stream, the particular part of the secondstream may be allowed to reach the first computing device.

In some examples, receiving the request to access the content of theelement by Step 1002 may comprise accessing a stream of digitalcommunication data sent by a first computing device to a secondcomputing device (for example as described above), and excluding accessto the first portion of the content of the element by Step 1008 maycomprise modifying at least part of the stream of digital communicationdata to obtain a modified stream, and providing the modified stream tothe second computing device. For example, the at least part of thestream of digital communication data may be modified to omit or tomodify the request to access the content of the element of Step 1002.

In some examples, receiving the request to access the content of theelement by Step 1002 may comprise accessing a stream of digitalcommunication data sent by a first computing device to a secondcomputing device (for example as described above), step 1006 maycomprise, in response to the first value in the permission record,allowing at least part of the stream of digital communication data toreach the second computing device, and method 1000 may further comprisein response to a third value in the permission record, blocking the atleast part of the stream of digital communication data from reaching thesecond computing device. For example, the third value may be identicalto the second value of Step 1008, may differ from the second value ofStep 1008, may differ from the first value of Step 1006, and so forth.

FIG. 11 illustrates an example of a method 1100 for ownershipdetermination, for example for ownership determination in privacyfirewalls. In this example, method 1100 may comprise: receiving arequest of a user to perform an action for creating a new datacollection using one or more source data collections (Step 1102),accessing one or more ownership records to determine ownership status ofthe one or more source data collections (Step 1104), accessing one ormore permission records to determine permission status of the user inrelation to the one or more source data collections (Step 1106), inresponse to a determination that the user does not have permission toview at least part of at least one of the one or more source datacollections and that the user is not an owner of the at least one of theone or more source data collections, determining that the user is not anowner of the new data collection (Step 1108), and in response to adetermination that for each data collection of the one or more sourcedata collections the user is at least one of an owner of the datacollection or has permission to view the entire data collection,determining that the user is an owner of the new data collection (Step1110). In some implementations, method 1100 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. In some implementations, one or more steps illustrated inFIG. 11 may be executed in a different order and/or one or more groupsof steps may be executed simultaneously and/or a plurality of steps maybe combined into single step and/or a single step may be broken down toa plurality of steps.

In some examples, method 1100 may further comprise performing the action(of Step 1102) for creating the new data collection using the one ormore source data collections. In some examples, the action (of Step1102) for creating the new data collection using the one or more sourcedata collections may comprise selecting a parameter for optimizing afunction of data included in the one or more source data collections,and in one example, the new data collection may include at least onevalue of the optimized function. Some non-limiting examples of suchparameters may include a hyper-parameter (for example, of an artificialneural network, of a machine learning algorithm, of a kernel function,of a family of functions, etc.), a power and/or a base of an exponentexpression, a numerator, a denominator, and so forth. In some examples,the action (of Step 1102) for creating the new data collection using theone or more source data collections may comprise calculating a value ofa function of data included in the one or more source data collections,and in one example, the new data collection may include the calculatedvalue of the function of the data. Some non-limiting examples of suchfunction may include a classification function, a regression function, afunction learnt using a machine learning model, an artificial neuralnetwork, a polynomial function, an exponential function, a linearfunction, a non-linear function, and so forth. In some examples, theaction (of Step 1102) for creating the new data collection using the oneor more source data collections may comprise calculating a gradient of afunction of data included in the one or more source data collections.Some non-limiting examples of such function may include a classificationfunction, a regression function, an intermediate function in anoptimization and/or a machine learning algorithm learning process, afunction learnt using a machine learning model, an artificial neuralnetwork, a polynomial function, an exponential function, a linearfunction, a non-linear function, and so forth. In one example, the newdata collection may include the calculated gradient of the function ofdata. In another example, the new data collection may be based on thecalculated gradient of the function of data. For example, in response toa first calculated gradient of the function of data, a first new datacollection may be generated, and in response to a second calculatedgradient of the function of data, a second new data collection may begenerated, the second new data collection may differ from the first newdata collection. In some examples, the action (of Step 1102) forcreating the new data collection using the one or more source datacollections may comprise calculating a statistical measurement of dataincluded in the one or more source data collections, and in one example,the new data collection may include the calculated statisticalmeasurement. Some non-limiting examples of such statistical measurementmay include mean, median, mode, variance, standard deviation, histogram,entropy, and so forth. In some examples, method 1100 may furthercomprise, for example in response to a determination by Step 1106 thatthe user does not have permission to use at least part of the one ormore source data collections to create different data collections,denying the request received by Step 1102.

In some examples, method 1100 may further comprise updating the one ormore ownership records accessed by Step 1104 based on the determinationof whether the user is an owner of the new data collection. For example,in response to a determination that the user is an owner of the new datacollection, method 1100 may make a first update to the one or moreownership records accessed by Step 1104, and in response to adetermination that the user is not an owner of the new data collection,method 1100 may make a second update to the one or more ownershiprecords accessed by Step 1104, the second update may differ from thefirst update. In another example, in response to a determination thatthe user is an owner of the new data collection, method 1100 may make afirst update to the one or more ownership records accessed by Step 1104,and in response to a determination that the user is not an owner of thenew data collection, method 1100 may forgo making the first update tothe one or more ownership records accessed by Step 1104.

In some examples, method 1100 may further comprise updating the one ormore permission records accessed by Step 1106 based on the determinationof whether the user is an owner of the new data collection determinedusing Step 1104 and/or based on the permission status of the user inrelation to the one or more source data collections determined by Step1106. For example, in response to a determination that the user is anowner of the new data collection, method 1100 may make a first update tothe one or more permission records accessed by Step 1106, and inresponse to a determination that the user is not an owner of the newdata collection, method 1100 may make a second update to the one or morepermission records accessed by Step 1106, the second update may differfrom the first update. In another example, in response to adetermination that the user is an owner of the new data collection,method 1100 may make a first update to the one or more permissionrecords accessed by Step 1106, and in response to a determination thatthe user is not an owner of the new data collection, method 1100 mayforgo making the first update to the one or more permission recordsaccessed by Step 1106. In yet another example, in response to a firstpermission status of the user in relation to the one or more source datacollections, method 1100 may make a first update to the one or morepermission records accessed by Step 1106, and in response to a secondpermission status of the user in relation to the one or more source datacollections, method 1100 may make a second update to the one or morepermission records accessed by Step 1106, the second update may differfrom the first update. In an additional example, in response to a firstpermission status of the user in relation to the one or more source datacollections, method 1100 may make a first update to the one or morepermission records accessed by Step 1106, and in response to a secondpermission status of the user in relation to the one or more source datacollections, method 1100 may forgo making the first update to the one ormore permission records accessed by Step 1106. In yet another example,in response to a first combination of ownership status determined byStep 1104 and permission status determined by Step 1106, method 1100 maymake a first update to the one or more permission records accessed byStep 1106, and in response to a second combination of ownership statusdetermined by Step 1104 and permission status determined by Step 1106,method 1100 may make a second update to the one or more permissionrecords accessed by Step 1106, the second update may differ from thefirst update. In an additional example, in response to a firstcombination of ownership status determined by Step 1104 and permissionstatus determined by Step 1106, method 1100 may make a first update tothe one or more permission records accessed by Step 1106, and inresponse to a second combination of ownership status determined by Step1104 and permission status determined by Step 1106, method 1100 mayforgo making the first update to the one or more permission recordsaccessed by Step 1106.

In some examples, the one or more source data collections of Step 1102may include a plurality of records, the action (of Step 1102) forcreating the new data collection using the one or more source datacollections may comprise training a machine learning model using a firstportion of the plurality of records, and the new data collection of Step1102 may include at least a prediction of the trained machine learningmodel for at least one record of the plurality of records not includedin the first portion of the plurality of records.

In some examples, Step 1102 may comprise receiving a request of a userto perform an action for creating a new data collection using one ormore source data collections. In one example, Step 1102 may read therequest from a memory (such as memory units 210, shared memory modules410, and so forth). In another example, Step 1102 may receive therequest from an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth). In yet another example, Step 1102 may receive the request fromthe user (for example, through a user interface, through a web page,using an input device, and so forth).

In some examples, receiving the request of the user by Step 1102 maycomprise accessing a stream of digital communication data sent by afirst computing device to a second computing device, and analyzing thestream to detect the request in the stream, for example using a patternmatching algorithm, by parsing a protocol used for communication overthe stream, and so forth. In one example, the analysis of the stream maybe performed by a third computing device, the third computing device maydiffer from the first computing device and from the second computingdevice. In another example, the analysis of the stream may be performedby the second computing device. In yet another example, the analysis ofthe stream may be performed by the first computing device. In oneexample, the second computing device may be configured to perform theaction for creating the new data collection using the one or more sourcedata collections in response to the request of the user. In one example,the stream of digital communication data may be accessed by a privacyfirewall, for example as described above. In another example, the streamof digital communication data may be read from a memory (such as memoryunits 210, shared memory modules 410, and so forth), may be receivedfrom an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth), may be received from a communication network using networksniffing techniques, and so forth.

In some examples, Step 1104 may comprise accessing one or more ownershiprecords to determine ownership status of the one or more source datacollections of Step 1102. For example Step 1104 may access the one ormore ownership records in a memory (such as memory units 210, sharedmemory modules 410, and so forth). In another example, Step 1104 mayaccess the one or more ownership records at an external device over acommunication network using a communication device (such ascommunication modules 230, internal communication modules 440, externalcommunication modules 450, and so forth). For example, the ownershiprecords may include an identifier of an owner of a source datacollection for at least some of the one or more source data collections,may include type of ownership corresponding to a source data collectionfor at least some of the one or more source data collections, and soforth. Some non-limiting examples of such type of ownership may include‘full ownership’, ‘conditional ownership’, ‘sole ownership’, ‘jointownership’, ‘ownership due to creation’, ‘ownership due to assignment’,and so forth. In some examples, the ownership status may include atleast one of the identities of the owners, the type of the ownership, acategory indicative of the owners, and so forth.

In some examples, Step 1106 may comprise accessing one or morepermission records to determine permission status of the user of Step1102 in relation to the one or more source data collections of Step1102. For example Step 1106 may access the one or more permissionrecords in a memory (such as memory units 210, shared memory modules410, and so forth). In another example, Step 1106 may access the one ormore permission records at an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In yet another example, Step 1106 may access the one ormore permission records in permissions 716. For example, the permissionrecords may include an indication of a permission corresponding to auser (or a group of users that including the user) and a source datacollection for at least some of the one or more source data collections.Other examples are described above, for example with relation topermissions 716. In some examples, the permission status may include atleast one of a permission corresponding to the user of Step 1102 and atleast one of the one or more source data collections of Step 1102 (suchas ‘read only’, ‘edit’, etc.), a type of the permission (such as‘temporary’, ‘permanent’, etc.), and so forth.

In some examples, Step 1108 may comprise, for example in response to adetermination by Step 1106 that the user of Step 1102 does not havepermission to view at least part of at least one of the one or moresource data collections of Step 1102 and to a determination by Step 1104that the user of Step 1102 is not an owner of the at least one of theone or more source data collections of Step 1102, determining that theuser of Step 1102 is not an owner of the new data collection of Step1102.

In some examples, Step 1110 may comprise, for example in response to adetermination by Step 1104 and/or Step 1106 that for each datacollection of the one or more source data collections of Step 1102 theuser of Step 1102 is at least one of an owner of the data collection orhas permission to view the entire data collection, determining that theuser of Step 1102 is an owner of the new data collection.

In some examples, method 1100 may further comprise determining at leastone owner of the new data collection, for example based on the ownershipstatus of the one or more source data collections determined by Step1104 and/or based on the permission status of the user in relation tothe one or more source data collections determined by Step 1106. Thedetermined at least one owner may include the user, may not include theuser, may include the user and at least one additional entity, may bethe user, may be a single owner, may be a plurality of owners, and soforth. For example, in response to a first ownership status, method 1100may determining a first at least one owner of the new data collection,and in response to a second ownership status, method 1100 maydetermining a second at least one owner of the new data collection, thesecond at least one owner of the new data collection may differ from thefirst at least one owner of the new data collection. In another example,in response to a first combination of ownership status and permissionstatus, method 1100 may determining a first at least one owner of thenew data collection, and in response to a second combination ofownership status and permission status, method 1100 may determining asecond at least one owner of the new data collection, the second atleast one owner of the new data collection may differ from the first atleast one owner of the new data collection. In yet another example, inresponse to a first permission status, method 1100 may determining afirst at least one owner of the new data collection, and in response toa second permission status, method 1100 may determining a second atleast one owner of the new data collection, the second at least oneowner of the new data collection may differ from the first at least oneowner of the new data collection.

In some examples, method 1100 may further comprise determining a size ofthe new data collection, Some non-limiting examples of such size of datacollection may include storage size of the data collection (for examplein bytes, bits, etc.), number of data-points in the data collection,entropy of the data collection, mathematical cardinality of the datacollection, mathematical dimension of the data collection,Vapnik-Chervonenkis dimension related to the data collection, and soforth. Further, in some examples, in response to a first size of the newdata collection, method 1100 may determine a first permission of theuser in relation to the new data collection, and in response to a secondsize of the new data collection, method 1100 may determine a secondpermission of the user in relation to the new data collection, thesecond permission may differ from the first permission. Somenon-limiting examples of such examples may include permission to modifythe new data collection, permission to delete the new data collection,permission to read data from the new data collection, permission toobtain statistical measurements of the new data collection, permissionto obtain synthetic data generated based on the new data collection, andso forth.

FIG. 12 illustrates an example of a method 1200 for determiningpermissions, for example for determining permissions in privacyfirewalls. In this example, method 1200 may comprise: analyzing at leastpart of a content of a data collection to determine a subject matter(Step 1202), determining a permission corresponding to the datacollection and at least one user based on the subject matter (Step1204), receiving a request of the at least one user to access at leastpart of the data collection (Step 1206), in response to a firstdetermined permission, providing the requested access to the at leastpart of the data collection (Step 1208), and in response to a seconddetermined permission, denying the request (Step 1210). In someimplementations, method 1200 may comprise one or more additional steps,while some of the steps listed above may be modified or excluded. Insome implementations, one or more steps illustrated in FIG. 12 may beexecuted in a different order and/or one or more groups of steps may beexecuted simultaneously and/or a plurality of steps may be combined intosingle step and/or a single step may be broken down to a plurality ofsteps.

In some examples, Step 1202 may comprise analyzing at least part of acontent of a data collection to determine a subject matter. For example,a machine learning model may be trained using training examples todetermine subject matter from content (for example, of datacollections), and Step 1202 may use the trained machine learning modelto analyze the at least part of a content of a data collection anddetermine the subject matter. An example of such training example mayinclude a sample content, together with a label indicating the subjectmatter. In another example, the at least part of a content of a datacollection may include textual information, and Natural LanguageProcessing algorithms (such as topic identification algorithms) may beused to analyze the textual information and determine the subject. Inyet another example, the at least part of a content of a data collectionmay include visual information, and computer vision algorithms may beused to analyze the visual information to determine the subject of thevisual information. In an additional example, a classification algorithmmay be used to classify the at least part of a content of a datacollection to one of a plurality of alternative subject matters.

In some examples, the at least part of the content of the datacollection of Step 1202 may include at least one or more medical images,and Step 1202 may comprise analyzing the one or more medical images todetermine the subject matter. In one example, a convolution of aplurality of pixels of the one or more medical images may be calculated,and Step 1202 may use the calculated convolution to determine thesubject matter. For example, in response to a first value of thecalculated convolution, Step 1202 may determine that a first subjectmatter corresponds to the data collection, and in response to a secondvalue of the calculated convolution, Step 1202 may determine that asecond subject matter corresponds to the data collection, the secondsubject matter may differ from the first subject matter. In one example,one or more pixels of at least one of the one or more medical images maybe analyzed to generate a transformed image, and Step 1202 may use thetransformed image to determine the subject matter. For example, inresponse to a first transformed image, Step 1202 may determine that afirst subject matter corresponds to the data collection, and in responseto a second transformed image, Step 1202 may determine that a secondsubject matter corresponds to the data collection, the second subjectmatter may differ from the first subject matter.

In some examples, the at least part of the content of the datacollection of Step 1202 may include at least textual information, andStep 1202 may comprise analyzing the textual information to determinethe subject matter. For example, in response to a first textualinformation, Step 1202 may determine that a first subject mattercorresponds to the data collection, and in response to a second textualinformation, Step 1202 may determine that a second subject mattercorresponds to the data collection, the second subject matter may differfrom the first subject matter. In one example, the textual informationmay be analyzed to determine word prevalence of at least one word in atleast a portion of the textual information, and Step 1202 may use thedetermined word prevalence to determine the subject matter. For example,in response to a first word prevalence, Step 1202 may determine that afirst subject matter corresponds to the data collection, and in responseto a second word prevalence, Step 1202 may determine that a secondsubject matter corresponds to the data collection, the second subjectmatter may differ from the first subject matter.

In some examples, the at least part of the content of the datacollection of Step 1202 may include at least one or more audiorecordings, and Step 1202 may comprise analyzing the one or more audiorecordings to determine the subject matter, for example using one ormore audio analysis algorithms. For example, in response to a firstaudio recording, Step 1202 may determine that a first subject mattercorresponds to the data collection, and in response to a second audiorecording, Step 1202 may determine that a second subject mattercorresponds to the data collection, the second subject matter may differfrom the first subject matter. In one example, a convolution of at leastpart of the one or more audio recordings may be calculated, and Step1202 may use the calculated convolution to determine the subject matter.For example, in response to a first value of the calculated convolution,Step 1202 may determine that a first subject matter corresponds to thedata collection, and in response to a second value of the calculatedconvolution, Step 1202 may determine that a second subject mattercorresponds to the data collection, the second subject matter may differfrom the first subject matter.

In some examples, Step 1204 may comprise determining a permissioncorresponding to the data collection of Step 1202 and at least one userbased on the subject matter determined by Step 1202. Some non-limitingexamples of such permission may include permission for the at least oneuser to modify the data collection, permission for the at least one userto delete the data collection, permission for the at least one user toread data from the data collection, permission for the at least one userto obtain statistical measurements of the data collection, permissionfor the at least one user to obtain synthetic data generated based onthe data collection, and so forth. For example, in response to a firstsubject matter determined by Step 1202, Step 1204 may determine a firstpermission corresponding to the data collection and the at least oneuser, and in response to a second subject matter determined by Step1202, Step 1204 may determine a second permission corresponding to thedata collection and the at least one user, the second permission maydiffer from the first permission.

In some examples, the subject matter may correspond to a body organ, inresponse to a first body organ corresponding to the subject matter, Step1204 may determine a first permission corresponding to the datacollection and the at least one user, and in response to a second bodyorgan corresponding to the subject matter, Step 1204 may determine asecond permission corresponding to the data collection and the at leastone user, the second permission may differ from the first permission. Insome examples, the subject matter may correspond to a medical specialty,in response to a first medical specialty corresponding to the subjectmatter, Step 1204 may determine a first permission corresponding to thedata collection and the at least one user, and in response to a secondmedical specialty corresponding to the subject matter, Step 1204 maydetermine a second permission corresponding to the data collection andthe at least one user, the second permission may differ from the firstpermission. In some examples, the subject matter may correspond to adisease, in response to a first disease corresponding to the subjectmatter, Step 1204 may determine a first permission corresponding to thedata collection and the at least one user, and in response to a seconddisease corresponding to the subject matter, Step 1204 may determine asecond permission corresponding to the data collection and the at leastone user, the second permission may differ from the first permission. Insome examples, the subject matter may correspond to a medical condition,in response to a first medical condition corresponding to the subjectmatter, Step 1204 may determine a first permission corresponding to thedata collection and the at least one user, and in response to a secondmedical condition corresponding to the subject matter, Step 1204 maydetermine a second permission corresponding to the data collection andthe at least one user, the second permission may differ from the firstpermission.

In some examples, Step 1204 may determine the permission correspondingto the data collection of Step 1202 and the at least one user based onthe subject matter and a property of the at least one user. For example,in response to a first combination of subject matter and property of theat least one user, Step 1204 may determine a first permissioncorresponding to the data collection and the at least one user, and inresponse to a second combination of subject matter and property of theat least one user, Step 1204 may determine a second permissioncorresponding to the data collection and the at least one user, thesecond permission may differ from the first permission. Somenon-limiting examples of such property of the at least one user mayinclude an affiliation of the at least one user, a medical specialtycorresponding to the at least one user, an demographic detail of the atleast one user, a geographic location corresponding to the at least oneuser, payment data corresponding to the at least one user, privileges ofthe at least one user, past behavior of the at least one user, and soforth. For example, the property of the at least one user may be anaffiliation of the at least one user, in response to a first combinationof subject matter and affiliation of the at least one user, Step 1204may determine a first permission corresponding to the data collectionand the at least one user, and in response to a second combination ofsubject matter and affiliation of the at least one user, Step 1204 maydetermine a second permission corresponding to the data collection andthe at least one user, the second permission may differ from the firstpermission. In another example, the property of the at least one usermay be a medical specialty corresponding to the at least one user, inresponse to a first combination of subject matter and medical specialtycorresponding to the at least one user, Step 1204 may determine a firstpermission corresponding to the data collection and the at least oneuser, and in response to a second combination of subject matter andmedical specialty corresponding to the at least one user, Step 1204 maydetermine a second permission corresponding to the data collection andthe at least one user, the second permission may differ from the firstpermission.

In some examples, Step 1206 may comprise receiving a request of the atleast one user of Step 1204 to access at least part of the datacollection of Step 1202. In one example, Step 1206 may read the requestfrom a memory (such as memory units 210, shared memory modules 410, andso forth). In another example, Step 1206 may receive the request from anexternal device over a communication network using a communicationdevice (such as communication modules 230, internal communicationmodules 440, external communication modules 450, and so forth). In yetanother example, Step 1206 may receive the request from a user (forexample, through a user interface, through a web page, using an inputdevice, and so forth).

In some examples, receiving the request of the user by Step 1206 maycomprise accessing a stream of digital communication data sent by afirst computing device to a second computing device, and analyzing thestream to detect the request in the stream, for example using a patternmatching algorithm, by parsing a protocol used for communication overthe stream, and so forth. In one example, the analysis of the stream maybe performed by a third computing device, the third computing device maydiffer from the first computing device and from the second computingdevice. In another example, the analysis of the stream may be performedby the second computing device. In yet another example, the analysis ofthe stream may be performed by the first computing device. In oneexample, the second computing device may be configured to access the atleast part of the data collection in response to the request of theuser. In one example, the stream of digital communication data may beaccessed by a privacy firewall, for example as described above. Inanother example, the stream of digital communication data may be readfrom a memory (such as memory units 210, shared memory modules 410, andso forth), may be received from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth), may be received from a communication network usingnetwork sniffing techniques, and so forth.

In some examples, Step 1208 may comprise, for example in response to afirst determined permission, providing the requested access to the atleast part of the data collection. In some examples, Step 1210 maycomprise, for example in response to a second determined permission,denying the request received by Step 1206 to access the at least part ofthe data collection. In some examples, the data collection of Step 1202may include a first portion and a second portion, the first portion mayinclude identifiable information and the second portion may include noidentifiable information, in response to a third determined permission,method 1200 may provide access to the content of the data collection,including access to the first portion and the second portion of the datacollection, and in response to a fourth determined permission, method1200 may provide partial access to the data collection, the partialaccess may include access to the second portion of the data collectionand may exclude access to the first portion of the data collection.

In some examples, method 1200 may further comprise, for example inresponse to a third determined permission, providing access to syntheticinformation based on the data collection of Step 1202. In some examples,the synthetic information may be a result of analyzing the content ofthe data collection of Step 1202 to generate the synthetic information.For example, the content of the data collection of Step 1202 may beanalyzed to determine a distribution of values in particular portions ofthe data collection, and the determined distribution of values may beused to generate the synthetic information. In another example, thecontent of the data collection of Step 1202 may be used to train aGenerative Adversarial Network (GAN), and the trained GAN may be used togenerate the synthetic information.

In some examples, method 1200 may further comprise, for example inresponse to a third determined permission, providing access tostatistical information based on the data collection. In some examples,the statistical information may be a result of analyzing content of thedata collection of Step 1202 or of a portion of the data collection ofStep 1202 to generate the statistical information. For example, thecontent of the data collection of Step 1202 or of the portion of thedata collection of Step 1202 may be analyzed to determine a distributionof values in the data collection or in the portion of the datacollection of Step 1202, and information may be provided based on thedetermined distribution of values.

In some examples, receiving the request of the user by Step 1206 maycomprise accessing a stream of digital communication data sent by afirst computing device to a second computing device (for example asdescribed above), and denying the request by Step 1210 may compriseaccessing a second stream of digital communication data (the secondstream of digital communication may be a stream sent by the secondcomputing device to the first computing device), analyzing the secondstream to detect information based on the at least part of the datacollection, and in response to a detection of the information based onthe at least part of the data collection, blocking at least part of thesecond stream from reaching the first computing device. In one example,the second stream of digital communication data may be accessed by aprivacy firewall, for example as described above. In another example,the second stream of digital communication data may be read from amemory (such as memory units 210, shared memory modules 410, and soforth), may be received from an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth), may be received from a communication network usingnetwork sniffing techniques, and so forth. In some examples, analyzingthe second stream to detect information based on the at least part ofthe data collection in the second stream may be performed using apattern matching algorithm, by parsing a protocol used for communicationover the stream, by using a machine learning model trained usingtraining examples to detect information in data streams that is based onparticular portions of data elements, and so forth. In some examples, inresponse to a detection of the information based on the at least part ofthe data collection in a particular part of the second stream, theparticular part of the second stream may be blocked from reaching thefirst computing device, and in response to no detection of theinformation based on the at least part of the data collection in theparticular part of the second stream, the particular part of the secondstream may be allowed to reach the first computing device.

In some examples, receiving the request of the user by Step 1206 maycomprise accessing a stream of digital communication data sent by afirst computing device to a second computing device (for example asdescribed above), and denying the request by Step 1210 may comprisemodifying at least part of the stream of digital communication data toobtain a modified stream, and providing the modified stream to thesecond computing device. For example, the at least part of the stream ofdigital communication data may be modified to omit or to modify therequest to access the at least part of the data collection of Step 1206.

In some examples, receiving the request of the user by Step 1206 maycomprise accessing a stream of digital communication data sent by afirst computing device to a second computing device (for example asdescribed above), and step 1208 may comprise, in response to the firstdetermined permission, allowing at least part of the stream of digitalcommunication data to reach the second computing device, and method 1200may further comprise in response to a third determined permission,blocking the at least part of the stream of digital communication datafrom reaching the second computing device. For example, the thirddetermined permission may be identical to the second determinedpermission of Step 1210, may differ from the second determinedpermission of Step 1210, may differ from the first determined permissionof Step 1208, and so forth.

FIG. 13 illustrates an example of a method 1300 for detecting identifiedinformation, for example for detecting identified information in privacyfirewalls. In this example, method 1300 may comprise: accessing a datacollection to identify a repeating field in the data collection (Step1302), analyzing content of the field in the data collection todetermine whether the field is likely to include information thatidentifies at least one particular individual (Step 1304), receiving anaccess request of a user (Step 1306), accessing a permission recordassociated with the user (Step 1308), in response to a determinationthat the field is likely to include information that identifies at leastone particular individual and a first value in the permission record,denying access of the user to at least part of the content of the fieldin the data collection (Step 1310), in response to a determination thatthe field is not likely to include information that identifies at leastone particular individual and the first value in the permission record,providing access of the user to the at least part of the content of thefield in the data collection (Step 1312), and in response to a secondvalue in the permission record, providing access of the user to the atleast part of the content of the field in the data collection (Step1314). In some implementations, method 1300 may comprise one or moreadditional steps, while some of the steps listed above may be modifiedor excluded. In some implementations, one or more steps illustrated inFIG. 13 may be executed in a different order and/or one or more groupsof steps may be executed simultaneously and/or a plurality of steps maybe combined into single step and/or a single step may be broken down toa plurality of steps.

In some examples, Step 1302 may comprise accessing a data collection toidentify a repeating field in the data collection. Some non-limitingexamples of such repeating field may include a row or a column in a datatable comprising a plurality of values corresponding to the same field,a field in a data structure where the data collection includes aplurality of instances of the data structure and therefore a pluralityof values of the field, a field in a form where the data collectionincludes a plurality of copies of the form and therefore a plurality ofvalues of the field, and so forth. In some non-limiting examples, therepeating field in the data collection may include data of patients,identifiers of patients, names of patients, phone numbers of patients,address of patients, demographic information of patients, data ofphysicians, identifiers of physicians, names of physicians, data ofappointments, time of appointments, participants of appointments, typesof appointments, data of medications, name of medication, dosages ofmedications, data of prescriptions, date of prescriptions, medicationsin prescriptions, data of offices, data of surgeries, data of medicaldevices, data of medical tests, and so forth.

In some examples, Step 1304 may comprise analyzing content of a field,such as the field identified as a repeating field in the data collectionby Step 1302, to determine whether the field is likely to includeinformation that identifies at least one particular individual. Forexample, Step 1304 may base the determination of whether the field islikely to include information that identifies at least one particularindividual on one or more of a type of the field, a distribution ofvalues in the field, values in the field, analysis of values in thefield, other fields, types of other fields, distributions of values inother fields, values in other fields, analysis of values in otherfields, and so forth. In some examples, Step 1304 may comprise analyzingthe content of the field in the data collection identified by Step 1302as a repeated field using a machine learning model to determine whetherthe field is likely to include information that identifies at least oneparticular individual. For example, a machine learning model may betrained using training examples to determine whether fields are likelyto include information that identifies individuals or particular type ofindividuals from the content of the fields, and Step 1304 may use thetrained machine learning model to analyze the content of the field anddetermine whether the field is likely to include information thatidentifies at least one particular individual. An example of suchtraining example may include a sample content of a sample field togetherwith a label indicating whether the sample field include informationthat identifies at least one particular individual.

In some examples, Step 1304 may comprise searching for keywords in thecontent of the field in the data collection identified by Step 1302 as arepeated field to determine whether the field is likely to includeinformation that identifies at least one particular individual. Forexample, in response to a detection of a particular keyword in thecontent of the field, Step 1304 may determine that the field is likelyto include information that identifies at least one particularindividual, and in response to no detection of the particular keyword inthe content of the field, Step 1304 may determine that the field is notlikely to include information that identifies at least one particularindividual. In another example, in response to a detection of aparticular keyword in the content of the field, Step 1304 may determinethat the field is not likely to include information that identifies atleast one particular individual, and in response to no detection of theparticular keyword in the content of the field, Step 1304 may determinethat the field is likely to include information that identifies at leastone particular individual. In yet another example, in response to adetection of a particular combination of keywords in the content of thefield, Step 1304 may determine that the field is likely to includeinformation that identifies at least one particular individual, and inresponse to no detection of the particular combination of keywords inthe content of the field, Step 1304 may determine that the field is notlikely to include information that identifies at least one particularindividual. In some examples, Step 1304 may comprise analyzing adistribution of values in the content of the field in the datacollection identified by Step 1302 as a repeated field to determinewhether the field is likely to include information that identifies atleast one particular individual. For example, in response to a firstdistribution of values in the content of the field, Step 1304 maydetermine that the field is likely to include information thatidentifies at least one particular individual, and in response to asecond distribution of values in the content of the field, Step 1304 maydetermine that the field is not likely to include information thatidentifies at least one particular individual. In some examples, Step1304 may comprise analyzing content of a second field in the datacollection of Step 1302 (for example, different than the field in thedata collection identified by Step 1302 as a repeated field) todetermine whether the field in the data collection identified by Step1302 as a repeated field is likely to include information thatidentifies at least one particular individual. For example, in responseto a first content of the second field, Step 1304 may determine that thefield is likely to include information that identifies at least oneparticular individual, and in response to a second content of the secondfield, Step 1304 may determine that the field is not likely to includeinformation that identifies at least one particular individual.

In some examples, the content of the field in the data collectionidentified by Step 1302 as a repeated field may include one or moreaudio recordings, and Step 1304 may analyze content of at least part ofthe one or more audio recordings to determine whether the field islikely to include information that identifies at least one particularindividual. For example, Step 1304 may use a speech detection algorithmto determine whether the at least part of the one or more audiorecordings includes a voice of a person, in response to a determinationthat the at least part of the one or more audio recordings includes avoice of a person, Step 1304 may determine that the field is likely toinclude information that identifies at least one particular individual,and in response to a determination that the at least part of the one ormore audio recordings does not include a voice of a person, Step 1304may determine that the field is not likely to include information thatidentifies at least one particular individual. In another example, Step1304 may use speech to text algorithms to obtain textual informationcorresponding to speech in the at least part of the one or more audiorecordings, and Step 1304 may analyze the obtained textual information,for example as described below, to determine whether the field is likelyto include information that identifies at least one particularindividual.

In some examples, the content of the field in the data collectionidentified by Step 1302 as a repeated field may include textualinformation, and Step 1304 may analyze content of at least part of thetextual information to determine whether the field is likely to includeinformation that identifies at least one particular individual. Forexample, keywords may be searched in the at least part of the textualinformation to determine whether the field is likely to includeinformation that identifies at least one particular individual, forexample as described above. In another example, Step 1304 may useNatural Language Processing (NLP) algorithms to analyze the at leastpart of the textual information and determine whether the field islikely to include information that identifies at least one particularindividual. In yet another example, a machine learning classificationmodel may be trained using training examples to determine whethertextual data includes identifying information of individuals, and Step1304 may use the trained machine learning classification model toanalyze the at least part of the textual information and determinewhether the field is likely to include information that identifies atleast one particular individual. An example of such training example mayinclude a sample textual information together with a label indicatingwhether the sample textual information includes information thatidentifies at least one particular individual.

In some examples, the content of the field in the data collectionidentified by Step 1302 as a repeated field includes numerical data, andStep 1304 may analyze content of at least part of the numerical data todetermine whether the field is likely to include information thatidentifies at least one particular individual. For example, a machinelearning classification model may be trained using training examples todetermine whether numerical data includes identifying information ofindividuals, and Step 1304 may use the trained machine learningclassification model to analyze the at least part of the numerical dataand determine whether the field is likely to include information thatidentifies at least one particular individual. An example of suchtraining example may include a sample numerical data together with alabel indicating whether the sample numerical data includes informationthat identifies at least one particular individual. In another example,distribution of values of the numerical data may be determined, inresponse to a first determined distribution, Step 1304 may determinethat the field is likely to include information that identifies at leastone particular individual, and in response to a second determineddistribution, Step 1304 may determine that the field is not likely toinclude information that identifies at least one particular individual.

In some examples, the content of the field in the data collectionidentified by Step 1302 as a repeated field may include one or moreimages, and Step 1304 may analyze the visual content of at least part ofthe one or more images to determine whether the field is likely toinclude information that identifies at least one particular individual.For example, an OCR algorithm may be used to extract textual informationfrom the at least part of the one or more images, and Step 1304 mayanalyze the extracted textual information (for example as describedabove) to determine whether the field is likely to include informationthat identifies at least one particular individual. In another example,face detection algorithms may be used to detect faces depicted in the atleast part of the one or more images, in response to a detection of aface in the at least part of the one or more images, Step 1304 maydetermine that the field is likely to include information thatidentifies at least one particular individual, and in response to nodetection of faces in the at least part of the one or more images, Step1304 may determine that the field is not likely to include informationthat identifies at least one particular individual. In yet anotherexample, face recognition algorithms may be used to recognizeindividuals depicted in the at least part of the one or more images, inresponse to a recognition of particular individuals in the at least partof the one or more images, Step 1304 may determine that the field islikely to include information that identifies at least one particularindividual, and in response to no recognition of the particularindividuals in the at least part of the one or more images, Step 1304may determine that the field is not likely to include information thatidentifies at least one particular individual. In one example, Step 1304may analyze the visual content of the at least part of the one or moreimages to determine whether the at least part of the one or more imagesincludes a depiction of a text (for example using text detectionalgorithms), in response to a determination that the at least part ofthe one or more images includes a depiction of a text, Step 1304 maydetermine that the field is likely to include information thatidentifies at least one particular individual, and in response to adetermination that the at least part of the one or more images does notinclude a depiction of a text, Step 1304 may determine that the field isnot likely to include information that identifies at least oneparticular individual. In one example, Step 1304 may analyze the visualcontent of the at least part of the one or more images to determinewhether the at least part of the one or more images includes a depictionof a face (for example using face detection algorithms), in response toa determination that the at least part of the one or more imagesincludes a depiction of a face, Step 1304 may determine that the fieldis likely to include information that identifies at least one particularindividual, and in response to a determination that the at least part ofthe one or more images does not include a depiction of a face, Step 1304may determine that the field is not likely to include information thatidentifies at least one particular individual. In one example, Step 1304may analyze the visual content of the at least part of the one or moreimages to determine whether the at least part of the one or more imagesincludes a depiction of a skin mark, and may analyze the depiction ofthe skin mark to determine whether the field is likely to includeinformation that identifies at least one particular individual. Somenon-limiting examples of such skin marks may include one or more ofscars, tattoos, birth marks, and so forth. For example, a visualdetector may be trained using training examples to detect skin marks inimages, and Step 1304 may use the trained visual detector to analyze thevisual content of the at least part of the one or more images todetermine whether the at least part of the one or more images includes adepiction of a skin mark. An example of such training example mayinclude a sample image together with a label indicating whether thesample image includes a skin mark and/or the location of the skin markin the sample image. For example, a machine learning classifier may betrained using training examples to determine whether skin marksidentifies at least one particular individual, and Step 1304 may use thetrained machine learning classifier to analyze the depiction of the skinmark to determine whether the field is likely to include informationthat identifies at least one particular individual. An example of suchtraining example may include a sample image of a skin mark, togetherwith a label indicating whether the skin mark depicted in the sampleimage identifies at least one particular individual. In some examples,Step 1304 may calculate a convolution of a plurality of pixels of the atleast part of the one or more images, and may use the calculatedconvolution to determine whether the field is likely to includeinformation that identifies at least one particular individual. Forexample, in response to a first value of the calculated convolution,Step 1304 may determine that the field is likely to include informationthat identifies at least one particular individual, and in response to asecond value of the calculated convolution, Step 1304 may determine thatthe field is not likely to include information that identifies at leastone particular individual. In some examples, Step 1304 may analyze oneor more pixels of the at least part of the one or more images togenerate a transformed image, and may use the transformed image todetermine whether the field is likely to include information thatidentifies at least one particular individual. For example, in responseto a first transformed image, Step 1304 may determine that the field islikely to include information that identifies at least one particularindividual, and in response to a second transformed image, Step 1304 maydetermine that the field is not likely to include information thatidentifies at least one particular individual.

In some examples, Step 1306 may comprise receiving an access request ofa user. In one example, Step 1306 may read the access request from amemory (such as memory units 210, shared memory modules 410, and soforth). In another example, Step 1306 may receive the access requestfrom an external device over a communication network using acommunication device (such as communication modules 230, internalcommunication modules 440, external communication modules 450, and soforth). In yet another example, Step 1306 may receive the access requestfrom the user (for example, through a user interface, through a webpage, using an input device, and so forth).

In some examples, receiving the access request of the user by Step 1306may comprise accessing a stream of digital communication data sent by afirst computing device to a second computing device, and analyzing thestream to detect the request in the stream, for example using a patternmatching algorithm, by parsing a protocol used for communication overthe stream, and so forth. In one example, the analysis of the stream maybe performed by a third computing device, the third computing device maydiffer from the first computing device and from the second computingdevice. In another example, the analysis of the stream may be performedby the second computing device. In yet another example, the analysis ofthe stream may be performed by the first computing device. In oneexample, the second computing device may be configured to access the atleast part of the content of the field in the data collection inresponse to the access request of the user. In one example, the streamof digital communication data may be accessed by a privacy firewall, forexample as described above. In another example, the stream of digitalcommunication data may be read from a memory (such as memory units 210,shared memory modules 410, and so forth), may be received from anexternal device over a communication network using a communicationdevice (such as communication modules 230, internal communicationmodules 440, external communication modules 450, and so forth), may bereceived from a communication network using network sniffing techniques,and so forth.

In some examples, Step 1308 may comprise accessing a permission recordassociated with the user of Step 1306. For example Step 1308 may accessthe permission record in a memory (such as memory units 210, sharedmemory modules 410, and so forth). In another example, Step 1308 mayaccess the permission record at an external device over a communicationnetwork using a communication device (such as communication modules 230,internal communication modules 440, external communication modules 450,and so forth). In yet another example, Step 1308 may access thepermission record in permissions 716. For example, the permission recordmay include an indication of a permission corresponding to the user (ora group of users that including the user) and the data collection ofStep 1302. Other examples are described above, for example with relationto permissions 716. In some examples, values in the permission recordmay include at least one of a permission corresponding to the user ofStep 1306 and the data collections of Step 1302 (such as ‘read only’,‘edit’, etc.), a type of the permission (such as ‘temporary’,‘permanent’, etc.), and so forth.

In some examples, Step 1310 may comprise, for example in response to adetermination by Step 1304 that the field identified by Step 1302 as arepeated field is likely to include information that identifies at leastone particular individual and a first value in the permission recordaccessed by Step 1308, denying access of the user of Step 1306 to atleast part of the content of the field in the data collection of Step1302. In some examples, Step 1312 may comprise, for example in responseto a determination by Step 1304 that the field identified by Step 1302as a repeated field is not likely to include information that identifiesat least one particular individual and the first value in the permissionrecord accessed by Step 1308, providing access of the user of Step 1306to the at least part of the content of the field in the data collectionof Step 1302. In some examples, Step 1314, for example in response to asecond value in the permission record accessed by Step 1308, providingaccess of the user of Step 1306 to the at least part of the content ofthe field in the data collection of Step 1302.

In some examples, the content of the field in the data collection may beused to generate a de-identified copy of the field. For example, thecontent of the field in the data collection may be analyzed usingpseudonymization or k-anonymization algorithms to generate thede-identified copy of the field. Further, in some examples, for examplein response to a determination by Step 1304 that the field identified byStep 1302 as a repeated field is likely to include information thatidentifies at least one particular individual and the first value in thepermission record, method 1300 may provide access to content of thegenerated de-identified copy of the field.

It will also be understood that the system according to the inventionmay be a suitably programmed computer, the computer including at least aprocessing unit and a memory unit. For example, the computer program canbe loaded onto the memory unit and can be executed by the processingunit. Likewise, the invention contemplates a software program beingreadable by a computer for executing the method of the invention. Theinvention further contemplates a non-transitory computer readable mediumstoring a software program comprising data and/or computer implementableinstruction for currying out any one or more of the methods describedabove.

What is claimed is:
 1. A non-transitory computer readable medium storinga software program comprising data and computer implementableinstructions for carrying out a method for detecting identifiedinformation, the method comprising: accessing a data collection toidentify a repeating field in the data collection; analyzing content ofthe field in the data collection to determine whether the field islikely to include information that identifies at least one particularindividual; receiving an access request of a user; accessing apermission record associated with the user; in response to adetermination that the field is likely to include information thatidentifies at least one particular individual and a first value in thepermission record, denying access of the user to at least part of thecontent of the field in the data collection; in response to adetermination that the field is not likely to include information thatidentifies at least one particular individual and the first value in thepermission record, providing access of the user to the at least part ofthe content of the field in the data collection; and in response to asecond value in the permission record, providing access of the user tothe at least part of the content of the field in the data collection. 2.The non-transitory computer readable medium of claim 1, whereinreceiving the access request of the user comprises: accessing a streamof digital communication data sent by a first computing device to asecond computing device; and analyzing the stream to detect the requestin the stream.
 3. The non-transitory computer readable medium of claim2, wherein the analysis of the stream is performed by the secondcomputing device.
 4. The non-transitory computer readable medium ofclaim 2, wherein the analysis of the stream is performed by a thirdcomputing device.
 5. The non-transitory computer readable medium ofclaim 1, wherein the method further comprises searching for keywords inthe content of the field in the data collection to determine whether thefield is likely to include information that identifies at least oneparticular individual.
 6. The non-transitory computer readable medium ofclaim 1, wherein the method further comprises analyzing a distributionof values in the content of the field in the data collection todetermine whether the field is likely to include information thatidentifies at least one particular individual.
 7. The non-transitorycomputer readable medium of claim 1, wherein the method furthercomprises analyzing the content of the field in the data collectionusing a machine learning model to determine whether the field is likelyto include information that identifies at least one particularindividual.
 8. The non-transitory computer readable medium of claim 1,wherein the method further comprises analyzing content of a second fieldin the data collection to determine whether the field is likely toinclude information that identifies at least one particular individual,wherein the second field differs from the field.
 9. The non-transitorycomputer readable medium of claim 1, wherein the method furthercomprises: using the content of the field in the data collection togenerate a de-identified copy of the field; and in response to adetermination that the field is likely to include information thatidentifies at least one particular individual and the first value in thepermission record, providing access to content of the generatedde-identified copy of the field.
 10. The non-transitory computerreadable medium of claim 1, wherein the content of the field in the datacollection includes one or more images, and the method further comprisesanalyzing visual content of at least part of the one or more images todetermine whether the field is likely to include information thatidentifies at least one particular individual.
 11. The non-transitorycomputer readable medium of claim 10, wherein the method furthercomprises: analyzing the visual content of the at least part of the oneor more images to determine whether the at least part of the one or moreimages includes a depiction of a text; and in response to adetermination that the at least part of the one or more images includesa depiction of a text, determining that the field is likely to includeinformation that identifies at least one particular individual.
 12. Thenon-transitory computer readable medium of claim 10, wherein the methodfurther comprises: analyzing the visual content of the at least part ofthe one or more images to determine whether the at least part of the oneor more images includes a depiction of a face; and in response to adetermination that the at least part of the one or more images includesa depiction of a face, determining that the field is likely to includeinformation that identifies at least one particular individual.
 13. Thenon-transitory computer readable medium of claim 10, wherein the methodfurther comprises: analyzing the visual content of the at least part ofthe one or more images to determine whether the at least part of the oneor more images includes a depiction of a skin mark; and analyzing thedepiction of the skin mark to determine whether the field is likely toinclude information that identifies at least one particular individual.14. The non-transitory computer readable medium of claim 10, wherein themethod further comprises: calculating a convolution of a plurality ofpixels of the at least part of the one or more images; and using thecalculated convolution to determine whether the field is likely toinclude information that identifies at least one particular individual.15. The non-transitory computer readable medium of claim 10, wherein themethod further comprises: analyzing one or more pixels of the at leastpart of the one or more images to generate a transformed image; andusing the transformed image to determine whether the field is likely toinclude information that identifies at least one particular individual.16. The non-transitory computer readable medium of claim 1, wherein thecontent of the field in the data collection includes one or more audiorecordings, and the method further comprises analyzing content of atleast part of the one or more audio recordings to determine whether thefield is likely to include information that identifies at least oneparticular individual.
 17. The non-transitory computer readable mediumof claim 1, wherein the content of the field in the data collectionincludes textual information, and the method further comprises analyzingcontent of at least part of the textual information to determine whetherthe field is likely to include information that identifies at least oneparticular individual.
 18. The non-transitory computer readable mediumof claim 1, wherein the content of the field in the data collectionincludes numerical data, and the method further comprises analyzingcontent of at least part of the numerical data to determine whether thefield is likely to include information that identifies at least oneparticular individual.
 19. A system for detecting identifiedinformation, the system comprising: at least one processing unitconfigured to: access a data collection to identify a repeating field inthe data collection; analyze content of the field in the data collectionto determine whether the field is likely to include information thatidentifies at least one particular individual; receive an access requestof a user; access a permission record associated with the user; inresponse to a determination that the field is likely to includeinformation that identifies at least one particular individual and afirst value in the permission record, deny access of the user to atleast part of the content of the field in the data collection; inresponse to a determination that the field is not likely to includeinformation that identifies at least one particular individual and thefirst value in the permission record, provide access of the user to theat least part of the content of the field in the data collection; and inresponse to a second value in the permission record, provide access ofthe user to the at least part of the content of the field in the datacollection.
 20. A method for detecting identified information, themethod comprising: accessing a data collection to identify a repeatingfield in the data collection; analyzing content of the field in the datacollection to determine whether the field is likely to includeinformation that identifies at least one particular individual;receiving an access request of a user; accessing a permission recordassociated with the user; in response to a determination that the fieldis likely to include information that identifies at least one particularindividual and a first value in the permission record, denying access ofthe user to at least part of the content of the field in the datacollection; in response to a determination that the field is not likelyto include information that identifies at least one particularindividual and the first value in the permission record, providingaccess of the user to the at least part of the content of the field inthe data collection; and in response to a second value in the permissionrecord, providing access of the user to the at least part of the contentof the field in the data collection.